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Science

Algorithms to Live By

Brian Christian, Tom Griffiths · 2016 · 10 ideas · 10 min

Computer science has already solved many of the everyday problems that feel uniquely human — when to stop searching, when to explore versus exploit, how to sort a messy life — and its algorithms make surprisingly good life advice.

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Why this book

Christian and Griffiths' central claim is that classic problems in computer science — optimal stopping, caching, scheduling, sorting — are formally identical to everyday human dilemmas like choosing an apartment, deciding when to stop dating around, or managing an overflowing inbox. Because computer scientists have spent decades proving what the mathematically best strategies are for these problems, we can borrow their answers instead of relying on gut feeling or folk wisdom.

The book matters because it reframes anxiety-inducing decisions as solved problems in disguise: not "how do I know if I've found the one" but "this is the secretary problem, and here's the provably optimal stopping rule." That reframe is often genuinely calming, and sometimes genuinely useful.

Who should read it

Read this if you like a rigorous excuse for the messy choices you already make — leaving your desk cluttered, procrastinating strategically, or settling on "good enough" instead of optimal. It's also a great entry point into computer science for people who assume it's only relevant to programmers.

About the author

Brian Christian is an American writer and poet with degrees in computer science, philosophy, and poetry, and Tom Griffiths is a cognitive scientist and professor (at UC Berkeley and later Princeton) who studies how human minds solve computational problems; they co-wrote the book, published in 2016.

The ideas

01The 37% rule tells you when to stop looking and choose02Explore/exploit trade-offs explain why trying new things has a shelf life03Sorting algorithms reveal why organizing everything is a bad use of time04Caching explains why your desk (and your memory) get messy in a smart way05Scheduling theory shows why doing the quickest task first often beats doing the most important one06Bayesian reasoning tells you how much to trust a prediction based on context07Overfitting explains why simpler plans often outperform complex ones08Relaxation shows why solving an easier version of your problem first is a legitimate strategy09Randomized algorithms show why a bit of randomness can beat careful deliberation10Networking theory reframes politeness and protocol as solved computational problems
computer-sciencedecision-makingalgorithmsproductivitypsychology
About this summary. Wisdomly re-expresses a book's ideas, arguments, and structure in our own words — nothing here is the author's text. Summaries are a map, not the territory: if the ideas land, the full book is worth your money and your evenings.