Algorithms to Live By The Computer Science of Human Decisions

A fascinating exploration of how insights from 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 issues for decades. And the solutions they've found have much to teach us.

In a dazzlingly interdisciplinary work, acclaimed author Brian Christian and cognitive scientist Tom Griffiths show how the 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 memory, Algorithms to Live By transforms the wisdom of computer science into strategies for human living.

Brian Christian is the author of The Most Human Human: What Artificial Intelligence Teaches Us About Being Alive, which was a Wall Street Journal bestseller and a New Yorker favorite book of the year. Alongside Steven Pinker and Daniel Kahneman, he was shortlisted for the Best Book of Ideas prize in the UK.

Tom Griffiths is a professor of psychology and cognitive science at UC Berkeley, where he directs the Institute of Cognitive and Brain Sciences.

Introduction 1
Algorithms to Live By

1 Optimal Stopping 9
When to Stop Looking

2 Explore/Exploit 31
The Latest vs. the Greatest

3 Sorting 59
Making Order

4 Caching 84
Forget About It

5 Scheduling 105
First Things First

6 Bayes’s Rule 128
Predicting the ­Future

7 Overfitting 149
When to Think Less

8 Relaxation 169
Let It Slide

9 Randomness 182
When to Leave It to Chance

10 Networking 205
How We Connect

11 Game Theory 229
The Minds of ­Others

Conclusion 256
Computational Kindness

Notes 263
Bibliography 315
Acknowledgments 335
Index 339

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