Algorithms to Live By: The Computer Science of Human Decisions
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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.
Customers say
Customers find the book thought-provoking, providing great insight into decision-making strategies, with one customer noting it serves as a good introduction to mathematical concepts. Moreover, the book has a great depth, with one review mentioning it covers a huge amount of material, and customers appreciate its humor and find it well worth the money. However, the readability receives mixed feedback, with some finding it an easy-to-read introduction while others note it can get too technical. Additionally, the algorithm content receives mixed reactions, with some appreciating the discussion of randomness while others find it too deep into algorithmic details.
fitzalling –
Mathematicians’ contributions to everyday problems
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.
James A. Thomason III –
Almost hits the mark
Annie Duke recommended this book (I forget if it was Thinking in Bets or Quit) and it sounded right up my alley. The nickel tour is that it was presented as attempting to apply various CS algorithms onto everyday life, to help us better interact with the world. Honestly, I have mixed feelings about the book – it’s trying to straddle between computer science and psychology, and it sort of ends up failing at both.So you get a high level super fast intro to various algorithms, and then frequently an even faster handwavy concept of how you might be able to use those things in your life. I found myself wanting more of both sides – I wanted a richer explanation of the algorithms, and I definitely wanted more clear applications of them. Some of the chapters were better than others – the initial one on Optimal Stopping provided very concrete examples of dealing with the Secretary Problem, the intros to Bayes’s Rule was useful, though I felt everything in that chapter was too quick. But towards the end of the book, both the chapters on Overfitting and Relaxation, as well as Randomness were rather insightful in some of the strategies they were proposing and how they can be useful. The final chapter on game theory did bring it all home nicely. I was nominally offended by how quickly he glossed over bucket sort, because it’s a pretty awesome algorithm. Its even cooler cousin, and my favorite, radix sort wasn’t mentioned at all. Heaps are probably too difficult to construct in the real world, but there was a fair bit of time dedicated to how stacks of papers on your desk is an optimal sorting strategy, so I felt ahead of the game in that regard.Overall, I’d call this a good-but-not-great book. I think it’s conceptually useful, but I also feel like I want to go off and dig into more about how to use these algorithms in the real world. I mean, I know tons of algorithms, but I hadn’t really encountered anything explicitly encouraging their use outside of software. I’m sure I use the knowledge in the real world, but I feel like it’s a little buried under the surface, so maybe this’ll help bring it out a little more. So I think I may have some follow up homework to do, but this was a nice intro. Recommended.
Sandy –
This is not a easy read. I completed the book and it took me some mental calisthenics to do so, but once you cross the bridge, you feel, the “epiphany”. The topic are varied and covers many maths and computer science related problems but they are actually real world issues. Topics like prisoners dilemma and Game theory are actually applied during difficult negotiations. Vickey auction is especially useful then the bidders don’t have a complete understanding of underling cost involved in running the business aka cost of capital for eg the cost of exploring an oil field or the cost of building a telecom network (often leading to under bidding). Win lose switch strategy, may be a good option when releasing a under trail drug which save lives but is not fully tested(case in the book was ECMO saving lives of children).Randomness, caching memory, overfitting are all discussed.My favourite chapter was ofcourse, Bayesian probability. Did you know the Bayes never published what would become his most famous accomplishment; his notes were edited and published posthumously by Richard Price. Furthermore it was de Laplace who came up with formula for probability ( r + 1 ) / ( n + 2 ).I sometimes wonder the life of a statistician where one sees probabilities and optimizations everywhere. In fact there is mention of how Tom leaves his socks lying near the bed to optimize caching, only to be admonished by his wife, for making a mess.All in all its a fantastic read, it will take some time to digest the material but once you internalize the concept your world view will change for ever. Happy reading
N. Walton –
I’ve read quite a lot of pop-sci books. One of my favourites: “Godel-Escher-Bach” really made me think hard about life and how things are interconnected. “Zen and the art of motorcycle maintenance” did the same. I think that a science book, written for the general populate which makes you genuinely stop and think is one that has fulfilled its purpose.When it comes to “pop-sci” computer science books, I think a lot of them are just banal listings of cool things people did: Turing, Babbage, IBM, etc etc. Yeah, computers are awesome, and a lot of very clever people did very clever things with them. Some AI or game theory books (like Rock Paper Scissors) are able to focus in on a few small areas and unravel them a little.However this book has absolutely opened my eyes. Like G,E,B’s “eternal golden braid”, the cover of the book says it all: Everything’s interconnected. There are some problems which humans have been grappling with for millennia, along with some new ones which have only arisen since the advent of the motorcar, or the washing machine. Many of these problems have good, bad, ugly and downright crazy solutions. Once you mix in “love”, “anger”, “personal gain”, “altruism” and all the other factors, you’re led into a world of fuzzy logic, bizarre solutions, and some very very interesting stories.All of these stories, along with their underlying problems and paradoxes are brilliantly explained, wrapped together in a very logical, clear order. There’s nothing suffixed with “discussed later in this book”, everything is explained in the right order to lead from the simplest problems (those on a microscopic scale), to the hardest macroscopic ones (global economies and political policy). Amongst all these stories and problem domains, the author boils the problem down into a particular game theoretic procedure, or simply explains how it’s a twist on a simpler problem. As the book progresses, the braids get more and more tightly bound, showing how people use mutli-level decision trees. The discussion of how poker players “psych” each other out, and can trick each other into a variety of “level games” is truly inspiring. It solves the problem I always had with poker: “it’s just a game of chance, right?”. This explains that, no, actually, there’s a huge amount of psychology going on. It rounds it out by giving the one single most stark example of how simple psychology won a poker champion almost half a million dollars, leaving you agape at the simplicity and complexity all rolled into one.I have to say, I was engrossed in this book. I think everyone should read it, because it gives practical, simple advice on how to break out of “symmetrical” problems, and shows how you can get one-up on the other people by employing some simple strategies.Absolutely fantastic book.
明日は億万長者 –
If you are interested in something about algorithms, you should read this book. I read it both in English and in Japanese. My impression from the book is different in original and translation. Japanese translation is excellent, but if you want to have some insight from this book, English version is better for the Japanese. But you can check your English understanding, Japanese version is very helpful. I bought this book to increase my understanding how to program python. Because I want to brash up my knowledge of computer and algorithms. However, this book is very useful for brash up my life itself.
Mina F. Beshay –
The book is awesome and a must read.The packaging is not that great, but it’s acceptable.
Nic –
“Algorithms to live by” è un libro davvero illuminante che ci mostra come la scienza informatica può aiutarci a prendere decisioni migliori nella vita quotidiana. Gli autori, Brian Christian e Tom Griffiths, applicano i principi dell’informatica e dell’ottimizzazione al mondo reale, aiutandoci a risolvere problemi come la gestione del tempo, la scelta della casa ideale, la decisione di lasciare o meno un lavoro e molto altro ancora. La scrittura è chiara e accessibile, e gli esempi sono divertenti ed istruttivi. Se sei interessato ad applicare la logica delle scienze informatiche alla vita quotidiana, questo libro è una lettura obbligatoria. Lo consiglio vivamente a chiunque abbia una mente curiosa e aperta.