Despite being an East-coaster, I'm a member of the Long Now Foundation, which--when I'm asked to describe it--I usually say is like TED, but with a long term view and way better substance. The Long Now gives regular talks, and then puts those talks up in video and audio form for others, who couldn't be in attendance. I subscribe to the podcast in iTunes, and listen to it--along with other podcasts--on my way to and from work.

A few months ago, Brian Christian was the guest speaker, and gave a talk centered around the subject matter of his latest book: Algorithms to Live By. The talk was fascinating, and contained a nice mixture of computer science, statistics, and humor to win the crowd over, and Christian managed to do so without coming across as too "pop science."

I purchased the book that same week, and between juggling work responsibilities and twins, managed to carve out about an hour each night to read through it. There were chapters that held my interest, and chapters that didn't, but overall the book was a fantastic mix of how various computer science problems are also real work problems, and algorithms that solve one can be applied to the other as well.

The first thing that catches you in the book is the discussion of optimal stopping, and how given a decision that needs to be made, you should begin making your choice after 37% of the options have been mulled over, assuming any of the next decisions/options are better than the ones that came before. This is illustrated with the secretary problem, and you can see why the authors led with this example not just in the book, but also in the Long Now talk. It seems both crazy and fascinating to have a difficult decision boiled down to such a hard percentage. The authors then go over different variations of the problem, and show how slight alteration can bring the best outcome.

The authors (Christian and Tom Griffiths) then follow this up with a rapid succession of entertaining problems such as exploit/explore to determine whether you should go with something that you know, or try something new, as well as chapters on sorting, caching, and scheduling, giving messy desk people hope by showing that a stack of files on a desk where something searched for is retrieved and then placed on top of the pile will eventually result in the most optimized sorting methodology for the job, and reminding older, forgetful people that accumulation of knowledge can result in greater time to sift and retrieve that information, renaming so-called brain farts to caching misses.

The chapter on Bayes' rule is where things start to get a little bogged down, but only in the beginning. Eventually, the chapter turns into an explanation on forecasting, showing which various predictive methodologies should be used for which various distributions--even equating the Erlang distribution to politics.

The back half of the book isn't as tight or as entertaining as the parts that came before it, but overfitting was a great read to be perusing while Nate Silver was being hammered for his polling methodology in the most recent election, and the chapter on networking gave a great, easy-to-read introduction to how information networks differ from telephony. The authors then conclude the book with game theory, discussing the tragedy of the commons, and how, as a society, we could pursue better options in order to ensure mass participation in important initiatives.

As somebody who studies and works in computer science and mathematics, I can say that casual readers will likely get lost in some sections, but powering through or re-reading will get you on to the more entertaining sections. This is a great book that works as a science popularizer without injecting fluffy prose/concepts or dumbing the material down.

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.

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.

I got the audio version of this book a year ago. Every time I thought to dive in, I felt a mild quaking in my soul. Gah, this is gonna be so hard, I worried. As a mere mortal without any background in computer science, advanced mathematics, logic, or statistics and risk, I feared my reach exceeded my ability to grasp.

Well, I wasn’t 100% wrong. It was hard. However, I understood and I learned. Yes, I hit replay dozens of times, but I got it. (Of course, after I ran through the audio version twice, I ordered the book because I just had to have it in my library.)

I did not expect the multidisciplinary palette from which the authors created this work. While teaching me about optimization problems in computer science, I came better to understand mean-variance portfolio optimization, game theory, equilibrium strategies, and caching, just to name a very few. This book has great depth. Remarkably, it has even greater range.

When examining the algorithmic dances that computers do nanosecond by nanosecond, we are also examining how we make decisions every day. Should I stay on this jammed expressway? How long should I wait for a table at my favorite eatery? Is it better to do three small laundry loads per week or have one big laundry day? How should I best arrange all of these books on my shelves? If you are like me, you have experienced that frustrating little circle, spinning and spinning, as your computer tries to wrest a result from the digital universe or just from your hard drive. When you are waiting for a taxi or a train, you are experiencing a life-size version that little spinning circle. When do you chalk it and look for Plan B?

This book describes how computers solve their problems and at the same time it shows us how the problems computers solve are just like the ones we deal with and solve, day in and day out. This isn’t too shocking, since humans set up the computer decision-making trees in the first place. Still, when I am synthesizing many possibilities, or struggling with family schedule optimization problems, I really can’t wait to apply terms like “simulated annealing” and “the price of anarchy”.

At the end of the day, when my family members are all doing the equivalent of sticking a thumb drive in my ear and starting their respective downloads, instead of objecting with: “Wait a minute, one at a time, I have to think!”, it will bring me joy to say, “Don’t trigger a Bufferbloat, guys, no one wants a Tail Drop.”

Most fun fact I learned:

“In contrast to the widely held view that less processing reduces accuracy, the study of heuristics shows that less information, computation, and time can in fact improve accuracy.” My translation? Don’t forget to ask grandma what she thinks, it’s likely to be spot on.

I really loved this book. It is one I will return to often.

An algorithm is a rule for solving a particular problem, and typically the problem is to find the best way to do something. In the context of computing, the "theory of algorithms" is taught in college Computer Science departments. Of course, everyday life humans also think about how to do things well, and our understanding of how humans do this is taught in college Psychology departments. This book brings these together in a unique way. It has the style of much good contemporary "popular science", with easy reading 1-4 page portions explaining an idea, its brief history, and one or two examples. It covers a huge amount of material, and provides an authoritative first look at any of its topics the reader has not previously encountered.

At the detailed level, it passes two tests I apply to popular books on Probability, my own expertise. The secretary problem is discussed in many books, because of its cute solution, but it is very unrealistic -- this book is honest about the unrealism. And for Bayes rule, in place of the usual "false positives in medical tests" story they consider how to estimate chances for how long some entity will last. In this context, different priors (power law, Erlang, or Normal) lead to different heuristic rules, and so it is worth thinking carefully about past data or analogous contexts to formulate your prior.

Overall it provides a wonderful first look at its topics. 50 pages of end notes testify to academic seriousness, though explicit suggestions for what to read next on each topic would be helpful. My only quibble is that this story-and-example style doesn't really indicate the scope of real-world utility of each idea.

Disappointing. The book describes a number of classic, well known, problems from operations research and statistics. There are examples and some history but no algorithms, no formulae or step by step instruction and little explanation of how, or why, these solutions work. While there are some references and explanations in the Notes, but not enough information to apply these to real life.

Another fitting title for this book could be How to Think Like a Computer. It offers a fascinating exploration of both the origins of computing and the ways in which computer science mirrors human thought. On one level, it provides a concise history of how early computers were built and how their designers grappled with fundamental questions about cognition: How do we store and retrieve information? How do we process it efficiently? And how can those principles be translated into machines?

On another level, the book examines the reverse dynamic—how understanding computers can reshape the way we think. It invites readers to consider how computational logic, structure, and problem-solving approaches can influence human reasoning.

Overall, it is an engaging and thought-provoking read. While not directly applicable to my daily life, I found it both enjoyable and intellectually stimulating.

I totally enjoyed Algorithms to Live By: The Computer Science of Human Decisions from the first to the last page. My only regret is that there is a last page. I prefer that Brian Christian and Tom Griffiths had ended this book with a promise that this adventure would be continued. The authors introduce and describe in detail several algorithms utilized in computer sciences that are particularly useful in everyday human decision-making. In addition, as with Brian Christian's first and best-selling book, The Most Human Human, this book addresses interesting philosophical and psychological issues inherent in the human experience. Neither a mathematician nor a computer scientist, I found the book easy to read and to understand. When higher math concepts are introduced, the authors spend sufficient time explaining them. The greatest challenge the book poses for me is to stand back from some of my assumptions about decision-making, so that I could absorb new and more effective ways to approach decisions in my everyday life. That’s a challenge I have embraced and prospered from already. Can't wait for the authors to bring us their next book.

Concepts intertwined with computer science and made very easy, practical application in real life, I give a 5 star

Thanks

Nikesh Joshi

Awesome book and good quality.

Opened my mind up to solve issues from my life with an analytical approach

This book offers practical insights into how we can apply computer algorithms to our daily lives for more efficient living. It includes plenty of easy-to-understand examples, so you don't need to worry even if you are not familiar with computer science. Furthermore, the authors—who are experts in human cognition—blend these technical concepts with beautiful literary expressions in certain chapters, making the book incredibly fascinating and enjoyable to read.

A very enjoyable read, and plenty of food for thought. There is almost no mathematics and no programming in this book, so don't let the title put you off. The book uses the computational approach to problems to take the reader through its analysis of the everyday examples of various "algorithms to live by", meaning in most cases the computational effort and stress that humans undergo when they process information to make a decision.

If you have read or studied algorithms in a computer science context this book will not tax you, but it helps put computational complexity and decision making in a real-world context, and I found this entertaining and thought-provoking. It touches lightly on some of the key themes of algorithms such as complexity, hardness, tractability and more so on optimisation. I am not sure if these topics are dealt with fully enough to introduce them to readers that encounter them for the first time in this book, with the exception of optimisation, which is very well put. But if you are aware of these themes, it is nice to have the formal references in the discussions that the book takes you through.

In the early chapters, I had the impression that I was going to have to branch out to other reference material to research the example problems covered. Actually I didn't - though there's nothing to stop you doing that of course. The main diversion I made was writing a program to test the 37% rule for the "Secretary Problem".

The book is nicely sectioned into chapters and topics or themes within the chapters. This makes it easy to read continuously or to stop, think and look to external references if you need to, before returning to continue. I think that other than the subject matter itself, the structure of the book is excellent and wish that other books adopted a similar format this to help the reader mark their progress easily. It's a very good example of sign-posting through manageable chunks of text.

The Notes section is extensive. I read only some of them, but they are there for the reader's elucidation.