出版社: Henry Holt and Co.
副标题: The Computer Science of Human Decisions
出版年: 2016419
页数: 368
定价: USD 30.00
装帧: Hardcover
ISBN: 9781627790369
内容简介 · · · · · ·
A fascinating exploration of how insights from computer algorithms can be applied to our everyday lives, helping to solve common decisionmaking 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...
A fascinating exploration of how insights from computer algorithms can be applied to our everyday lives, helping to solve common decisionmaking 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.
作者简介 · · · · · ·
About the Author
Brian Christian is the author of The Most Human Human, a Wall Street Journal bestseller, New York Times editors’ choice, and a New Yorker favorite book of the year. His writing has appeared in The New Yorker, The Atlantic, Wired, The Wall Street Journal, The Guardian, and The Paris Review, as well as in scientific journals such as Cognitive Science, and has bee...
About the Author
Brian Christian is the author of The Most Human Human, a Wall Street Journal bestseller, New York Times editors’ choice, and a New Yorker favorite book of the year. His writing has appeared in The New Yorker, The Atlantic, Wired, The Wall Street Journal, The Guardian, and The Paris Review, as well as in scientific journals such as Cognitive Science, and has been translated into eleven languages. He lives in San Francisco.
Tom Griffiths is a professor of psychology and cognitive science at UC Berkeley, where he directs the Computational Cognitive Science Lab. He has published more than 150 scientific papers on topics ranging from cognitive psychology to cultural evolution, and has received awards from the National Science Foundation, the Sloan Foundation, the American Psychological Association, and the Psychonomic Society, among others. He lives in Berkeley.
目录 · · · · · ·
Algorithms to Live By
1 Optimal Stopping 9
When to Stop Looking
2 Explore/Exploit 31
The Latest vs. the Greatest
· · · · · · (更多)
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
· · · · · · (收起)
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Algorithms to Live By的话题 · · · · · · ( 全部 条 )
Algorithms to Live By的书评 · · · · · · ( 全部 10 条 )
用不到的，就扔了它吧！
这篇书评可能有关键情节透露
你遇到了一个问题——橱柜里塞满了裤子、衬衫和内衣。于是，你想：该整理整理了。这下你面前就出现了两个问题： 1. 哪些东西需要保留呢？ 2. 留下的东西应该如何摆放呢？ 如果你也像我一样选择困难，那这个存储问题一定会让你十分头疼。但幸运的是世界上有一群十分痴迷于存储问... (展开)数学家告诉你：何时谨慎观察，何时果敢行动？
这篇书评可能有关键情节透露
摘编  孙熙霁 假设你想在市区中心的单位附近租个房子，你会怎么做呢？是左顾右盼，谨慎一些，还是立刻先下手为强？ 理论上讲，认真调查、仔细斟酌是理性消费者的一大特征，但是诸如北上广这样的残酷市场，并没有给你留下多少权衡的机会和时间。随便逛街买个衣服，你尚且可以反... (展开)用算法找对象：有实用价值的算法入门，有点烧脑：4星《算法之美》
Just to keep me reading
生活不少领域都有算法
很适合非cs专业的科普读物
> 更多书评10篇
读书笔记 · · · · · ·
我来写笔记
iphyer (爱生活，爱技术，爱学习，爱家人)
An algorithm is just a finite sequence of steps used to solve a problem, and algorithms are much broader and older by far than the computer.20180205 02:43

Sharing points: 1. Algorithms are not confined to mathematics alone. When you cook a bread from a recipe, when you knit a sweater from a pattern, when you put a sharp edge on a piece of flint by executing a precise sequence of strikes with the end of an antler a key step in making fine stone tools, you are following an algorithm. algorithms have been a part of human technology ever since the S...
20170303 04:37
Sharing points:
1. Algorithms are not confined to mathematics alone. When you cook a bread from a recipe, when you knit a sweater from a pattern, when you put a sharp edge on a piece of flint by executing a precise sequence of strikes with the end of an antler a key step in making fine stone tools, you are following an algorithm. algorithms have been a part of human technology ever since the Stone Age. Algorithms is just a finite sequence of steps used to solve a problem.
2. Thinking algorithmically about the world, learning about the fundamental structure of the problems we face and about the properties of their solutions, can help us see how good we actually are, and better understand the errors that we make.
3. The optimal solution takes the form  "Look then  leap rule"  you set a predetermined amount of time for "looking" that is, exploring your options, gathering the data, in which categorically don't choose anyone, no matter how impressive. After that point, you enter the "leap" phase, prepare to instantly commit to anyone who outshines the best applicant you saw in the look phase.
4. 37% rule: look at the first 37% of applicants, choosing none, then be ready to leap for anyone better than all those you've seen so far. It turns out, following this optimal strategy ultimately gives us a 37% chance of hiring the best applicant
回应 20170303 04:37 
你以为作者写的是数学公式？ 才不是，作者写的是人生： Being sensitive to how much time you have left is exactly what the computer science of the explore/exploit dilemma suggests. We think of the young as stereotypically fickle; the old, stereotypically set in their ways. In fact, both are behaving completely appropriately with respect to their intervals. The deliberate honing of a social net work...
20170302 21:15
你以为作者写的是数学公式？ 才不是，作者写的是人生：
Being sensitive to how much time you have left is exactly what the computer science of the explore/exploit dilemma suggests. We think of the young as stereotypically fickle; the old, stereotypically set in their ways. In fact, both are behaving completely appropriately with respect to their intervals. The deliberate honing of a social net work down to the most meaningful relationship is the retional response to having less time to enjoy them.
The explore/ exploit tradeoff also tells us how to think about advice from our elders. When your grandfather tells you which restaurants are good, you should listen – these are pearls gleaned from decades of searching. But when he only goes to the same place at 5:00 PM, every day, you should feel free to explore other options, even though they’ll likely be worse.
6. Perhaps the deepest insight that comes from thinking about later life as a chance to exploit knowledge acquired over decades is this: life should get better over time. What an explorer trades off for knowledge is pleasure. The Gittins index and the Upper Confidence bound, as well as inflate the appeal of lesserknown options beyond what we actually expect, since pleasant surprises can pay off many times over. But at the same time, this means that exploration necessarily leads to being let down on most occasions. Shifting the bulk of one’s attention to one’s favorite things should increase quality of life. Carstensen has found that older people are generally more satisfied with their social networks, and other report levels of emotional wellbeing that are higher those of younger adults.
回应 20170302 21:15 
半截方糖 (求不得)
Algorithm is just a finite sequence of steps used to solve a problem. Lookthenleap rule: you set a predetermined amount of time for "looking" that is , exploring your options, gathering data  in which your categorically don't choose anyone, no matter how impressive. After that point, you enter the " leap" phase, prepared to instantly commit to anyone who outshines the best...20170206 00:22
Algorithm is just a finite sequence of steps used to solve a problem.
The balance is 37%.好了，可以拿去用了。剩下的部分是有时间的人的娱乐了。啊recreational reading，多么奢侈。Lookthenleap rule: you set a predetermined amount of time for "looking" that is , exploring your options, gathering data  in which your categorically don't choose anyone, no matter how impressive. After that point, you enter the " leap" phase, prepared to instantly commit to anyone who outshines the best applicant you saw in the look phase.
回应 20170206 00:22

iphyer (爱生活，爱技术，爱学习，爱家人)
An algorithm is just a finite sequence of steps used to solve a problem, and algorithms are much broader and older by far than the computer.20180205 02:43

半截方糖 (求不得)
Algorithm is just a finite sequence of steps used to solve a problem. Lookthenleap rule: you set a predetermined amount of time for "looking" that is , exploring your options, gathering data  in which your categorically don't choose anyone, no matter how impressive. After that point, you enter the " leap" phase, prepared to instantly commit to anyone who outshines the best...20170206 00:22
Algorithm is just a finite sequence of steps used to solve a problem.
The balance is 37%.好了，可以拿去用了。剩下的部分是有时间的人的娱乐了。啊recreational reading，多么奢侈。Lookthenleap rule: you set a predetermined amount of time for "looking" that is , exploring your options, gathering data  in which your categorically don't choose anyone, no matter how impressive. After that point, you enter the " leap" phase, prepared to instantly commit to anyone who outshines the best applicant you saw in the look phase.
回应 20170206 00:22 
Sharing points: 1. Algorithms are not confined to mathematics alone. When you cook a bread from a recipe, when you knit a sweater from a pattern, when you put a sharp edge on a piece of flint by executing a precise sequence of strikes with the end of an antler a key step in making fine stone tools, you are following an algorithm. algorithms have been a part of human technology ever since the S...
20170303 04:37
Sharing points:
1. Algorithms are not confined to mathematics alone. When you cook a bread from a recipe, when you knit a sweater from a pattern, when you put a sharp edge on a piece of flint by executing a precise sequence of strikes with the end of an antler a key step in making fine stone tools, you are following an algorithm. algorithms have been a part of human technology ever since the Stone Age. Algorithms is just a finite sequence of steps used to solve a problem.
2. Thinking algorithmically about the world, learning about the fundamental structure of the problems we face and about the properties of their solutions, can help us see how good we actually are, and better understand the errors that we make.
3. The optimal solution takes the form  "Look then  leap rule"  you set a predetermined amount of time for "looking" that is, exploring your options, gathering the data, in which categorically don't choose anyone, no matter how impressive. After that point, you enter the "leap" phase, prepare to instantly commit to anyone who outshines the best applicant you saw in the look phase.
4. 37% rule: look at the first 37% of applicants, choosing none, then be ready to leap for anyone better than all those you've seen so far. It turns out, following this optimal strategy ultimately gives us a 37% chance of hiring the best applicant
回应 20170303 04:37 
你以为作者写的是数学公式？ 才不是，作者写的是人生： Being sensitive to how much time you have left is exactly what the computer science of the explore/exploit dilemma suggests. We think of the young as stereotypically fickle; the old, stereotypically set in their ways. In fact, both are behaving completely appropriately with respect to their intervals. The deliberate honing of a social net work...
20170302 21:15
你以为作者写的是数学公式？ 才不是，作者写的是人生：
Being sensitive to how much time you have left is exactly what the computer science of the explore/exploit dilemma suggests. We think of the young as stereotypically fickle; the old, stereotypically set in their ways. In fact, both are behaving completely appropriately with respect to their intervals. The deliberate honing of a social net work down to the most meaningful relationship is the retional response to having less time to enjoy them.
The explore/ exploit tradeoff also tells us how to think about advice from our elders. When your grandfather tells you which restaurants are good, you should listen – these are pearls gleaned from decades of searching. But when he only goes to the same place at 5:00 PM, every day, you should feel free to explore other options, even though they’ll likely be worse.
6. Perhaps the deepest insight that comes from thinking about later life as a chance to exploit knowledge acquired over decades is this: life should get better over time. What an explorer trades off for knowledge is pleasure. The Gittins index and the Upper Confidence bound, as well as inflate the appeal of lesserknown options beyond what we actually expect, since pleasant surprises can pay off many times over. But at the same time, this means that exploration necessarily leads to being let down on most occasions. Shifting the bulk of one’s attention to one’s favorite things should increase quality of life. Carstensen has found that older people are generally more satisfied with their social networks, and other report levels of emotional wellbeing that are higher those of younger adults.
回应 20170302 21:15

iphyer (爱生活，爱技术，爱学习，爱家人)
An algorithm is just a finite sequence of steps used to solve a problem, and algorithms are much broader and older by far than the computer.20180205 02:43

Sharing points: 1. Algorithms are not confined to mathematics alone. When you cook a bread from a recipe, when you knit a sweater from a pattern, when you put a sharp edge on a piece of flint by executing a precise sequence of strikes with the end of an antler a key step in making fine stone tools, you are following an algorithm. algorithms have been a part of human technology ever since the S...
20170303 04:37
Sharing points:
1. Algorithms are not confined to mathematics alone. When you cook a bread from a recipe, when you knit a sweater from a pattern, when you put a sharp edge on a piece of flint by executing a precise sequence of strikes with the end of an antler a key step in making fine stone tools, you are following an algorithm. algorithms have been a part of human technology ever since the Stone Age. Algorithms is just a finite sequence of steps used to solve a problem.
2. Thinking algorithmically about the world, learning about the fundamental structure of the problems we face and about the properties of their solutions, can help us see how good we actually are, and better understand the errors that we make.
3. The optimal solution takes the form  "Look then  leap rule"  you set a predetermined amount of time for "looking" that is, exploring your options, gathering the data, in which categorically don't choose anyone, no matter how impressive. After that point, you enter the "leap" phase, prepare to instantly commit to anyone who outshines the best applicant you saw in the look phase.
4. 37% rule: look at the first 37% of applicants, choosing none, then be ready to leap for anyone better than all those you've seen so far. It turns out, following this optimal strategy ultimately gives us a 37% chance of hiring the best applicant
回应 20170303 04:37 
你以为作者写的是数学公式？ 才不是，作者写的是人生： Being sensitive to how much time you have left is exactly what the computer science of the explore/exploit dilemma suggests. We think of the young as stereotypically fickle; the old, stereotypically set in their ways. In fact, both are behaving completely appropriately with respect to their intervals. The deliberate honing of a social net work...
20170302 21:15
你以为作者写的是数学公式？ 才不是，作者写的是人生：
Being sensitive to how much time you have left is exactly what the computer science of the explore/exploit dilemma suggests. We think of the young as stereotypically fickle; the old, stereotypically set in their ways. In fact, both are behaving completely appropriately with respect to their intervals. The deliberate honing of a social net work down to the most meaningful relationship is the retional response to having less time to enjoy them.
The explore/ exploit tradeoff also tells us how to think about advice from our elders. When your grandfather tells you which restaurants are good, you should listen – these are pearls gleaned from decades of searching. But when he only goes to the same place at 5:00 PM, every day, you should feel free to explore other options, even though they’ll likely be worse.
6. Perhaps the deepest insight that comes from thinking about later life as a chance to exploit knowledge acquired over decades is this: life should get better over time. What an explorer trades off for knowledge is pleasure. The Gittins index and the Upper Confidence bound, as well as inflate the appeal of lesserknown options beyond what we actually expect, since pleasant surprises can pay off many times over. But at the same time, this means that exploration necessarily leads to being let down on most occasions. Shifting the bulk of one’s attention to one’s favorite things should increase quality of life. Carstensen has found that older people are generally more satisfied with their social networks, and other report levels of emotional wellbeing that are higher those of younger adults.
回应 20170302 21:15 
半截方糖 (求不得)
Algorithm is just a finite sequence of steps used to solve a problem. Lookthenleap rule: you set a predetermined amount of time for "looking" that is , exploring your options, gathering data  in which your categorically don't choose anyone, no matter how impressive. After that point, you enter the " leap" phase, prepared to instantly commit to anyone who outshines the best...20170206 00:22
Algorithm is just a finite sequence of steps used to solve a problem.
The balance is 37%.好了，可以拿去用了。剩下的部分是有时间的人的娱乐了。啊recreational reading，多么奢侈。Lookthenleap rule: you set a predetermined amount of time for "looking" that is , exploring your options, gathering data  in which your categorically don't choose anyone, no matter how impressive. After that point, you enter the " leap" phase, prepared to instantly commit to anyone who outshines the best applicant you saw in the look phase.
回应 20170206 00:22
论坛 · · · · · ·
本书有中文版吗？  来自兔兔没有人抱  4 回应  20180428 
“外文原版图书”（淘宝店名）店中有售，链接为：  来自本杰明  20170930 
在哪儿买这本书 · · · · · ·
这本书的其他版本 · · · · · · ( 全部4 )
 中信出版集团版 2018520 / 27人读过 / 有售
 William Collins版 201746 / 3人读过 / 有售
 行路出版版 20178
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订阅关于Algorithms to Live By的评论:
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1 有用 洛神。 20170111
白话版的计算机算法课程
1 有用 逆铭睡眼惺忪地 20170425
强烈推荐给非CS从业者：先以非常直观的方式介绍算法，然后提升到哲学高度和你谈人生。。。对于从业者来说也应该是不错的消遣读物
0 有用 Roseanna 20180107
作者的引述里有发现有趣滴苏~
0 有用 weihu 20160729
看看
1 有用 Amo Wu 20180209
37%規則：以買房為例，目標一年內，前 37% 的時間只看不買，在預算內了解一下市場上哪些房子你喜歡，哪些不喜歡，記住這個階段內你看到過的最滿意的那個，等到過了 37% 這個時間點，一旦遇到比前一階段那個最好的房子好，或者類似的房子，就毫不猶豫地買下來。數學家的時間管理思維：1. 最近截止日期優先 2. 如果最近截止日期優先法還是做不完，優先放棄佔用時間最長的任務 3. 如果牽涉到別人的等待時間，... 37%規則：以買房為例，目標一年內，前 37% 的時間只看不買，在預算內了解一下市場上哪些房子你喜歡，哪些不喜歡，記住這個階段內你看到過的最滿意的那個，等到過了 37% 這個時間點，一旦遇到比前一階段那個最好的房子好，或者類似的房子，就毫不猶豫地買下來。數學家的時間管理思維：1. 最近截止日期優先 2. 如果最近截止日期優先法還是做不完，優先放棄佔用時間最長的任務 3. 如果牽涉到別人的等待時間，則完成時間短的任務優先 4. 小事與要事的衡量公式，任務密度 = 重要程度 / 完成時間，然後按照任務的密度由高到低的順序去做。 (展开)
0 有用 Joincle 20180628
作者将计算机的算法运用在生活中例子。其中第1，2章写的真好。Optimal stopping提到的37%rule，Explore/Exploit写出了人永远在有限时间有限信息做最好选择的现实，短期利益和长期投资的mixed strategy。其他几章内容平时也接触到了，但还是有部分内容有趣，如，信息终端无处不在的sorting推荐，overfitting大数据不如人的知觉，Erlang分布还是第一... 作者将计算机的算法运用在生活中例子。其中第1，2章写的真好。Optimal stopping提到的37%rule，Explore/Exploit写出了人永远在有限时间有限信息做最好选择的现实，短期利益和长期投资的mixed strategy。其他几章内容平时也接触到了，但还是有部分内容有趣，如，信息终端无处不在的sorting推荐，overfitting大数据不如人的知觉，Erlang分布还是第一次听到，路由器的Bufferbloat犹如现实的交通堵塞，总之算法能写成这样有趣还是不错的。 (展开)
0 有用 没有昵称 20180622
通勤/厕所读物
0 有用 无产光辉指 20180531
作为CS专业的，读这本书的感觉是里面大部分的算法我都知道，但是如何用这些算法思考，内化成自己的思维方式，并应用到生活中，则是这本书的大亮点，我敢这么说，这本书比所有朋友圈教你做人&做事的鸡汤文的总和还要多得多，并且不但让你知其然，还用数学方法让你知其所以然。如果能结合道金斯《自私的基因》一起看则能彻底塑造你的科学世界观。不知道为什么会有人给中差评，如果你真的按照这本书里的方法论做到并坚持了，你离社... 作为CS专业的，读这本书的感觉是里面大部分的算法我都知道，但是如何用这些算法思考，内化成自己的思维方式，并应用到生活中，则是这本书的大亮点，我敢这么说，这本书比所有朋友圈教你做人&做事的鸡汤文的总和还要多得多，并且不但让你知其然，还用数学方法让你知其所以然。如果能结合道金斯《自私的基因》一起看则能彻底塑造你的科学世界观。不知道为什么会有人给中差评，如果你真的按照这本书里的方法论做到并坚持了，你离社会精英也不远了，纸上谈兵，掉书袋的当我没说。再多说一句，这书并不像有人说的只是适合文科生读的“科普读物”，这完全拉低了这本书的水准，如果完全算法不懂的，没那么容易懂，即使懂一点，理解也不深，比如过拟合这种问题，如果搞过机器学习会理解很深，否则能沾个边就不错了 (展开)
0 有用 ShD 20180526
Very good! A lot of intuitions for the already known algorithms. Things like ACK in netwoking. Prisoner's dillema is recursive none computable stuff..
0 有用 reneryu 20180522
人生真谛  秘书问题！