Status
Available
Call number
Collection
Publication
New York : Henry Holt and Company, [2016]
Description
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.--From dust jacket.… (more)
User reviews
LibraryThing member bragan
When a computer alphabetize a list of words, or fits a curve to a series of data points, or decides what it should keep handy in parts of its memory it can access quickly, what exactly is it doing? Well, it's using an algorithm, some set of instructions that give it the rules for how it should
And that's what this book is about: algorithms of the kind computers use, and their applications in the real world. Which, I admit, sounds dull. I suppose to most people, it may be dull. But, giant nerd that I am, I found it fascinating. Intellectually exciting, even. How amazing is it that a very small change in the requirements of a problem can alter the task of finding the best solution from a simple to an impossible one... or that, by going back to the simpler version of the problem and working from there, you can often come very, very close to that best solution, anyway? This book is full of things like that that made me go wow, from the notion that introducing randomness into calculations about non-random things can actually give better results, to guidelines on how to make the best possible guesses based on completely insufficient data, to the welcome confirmation that I'm already intuitively using the optimal methods for alphabetizing my bookshelves.
It's all wonderfully well-written, too: beautifully comprehensible and full of excellent examples. There's also, for my mind, exactly the right amount of math, as the authors talk us through careful mathematical thinking without ever getting bogged down in equations. Or computer code, for that matter. It's all just nice, clear, readable English.
Am I going to go out now and apply the algorithms discussed here towards improving my own life? Well... maybe. Did I come away from it feeling like I understand the world better? I think so. Did I come away feeling I understand more about the tools we have for understanding the world? Absolutely. I also may have come away regretting more than ever that I didn't major in computer science, because it really did just light up all kinds of nerdy areas in my brain.
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proceed in tackling the problem before it. Of course, for any given problem some algorithms may be more efficient or give better results than others, and a lot of computer science and a fair amount of mathematics is dedicated to finding the best algorithms for the problems we want solved. And those rules are often ones we humans can use, too, whether we're deciding which tasks to tackle first in order to miss the fewest deadlines, or re-organizing our closets, or deciding when to stop driving around looking for a better parking space.And that's what this book is about: algorithms of the kind computers use, and their applications in the real world. Which, I admit, sounds dull. I suppose to most people, it may be dull. But, giant nerd that I am, I found it fascinating. Intellectually exciting, even. How amazing is it that a very small change in the requirements of a problem can alter the task of finding the best solution from a simple to an impossible one... or that, by going back to the simpler version of the problem and working from there, you can often come very, very close to that best solution, anyway? This book is full of things like that that made me go wow, from the notion that introducing randomness into calculations about non-random things can actually give better results, to guidelines on how to make the best possible guesses based on completely insufficient data, to the welcome confirmation that I'm already intuitively using the optimal methods for alphabetizing my bookshelves.
It's all wonderfully well-written, too: beautifully comprehensible and full of excellent examples. There's also, for my mind, exactly the right amount of math, as the authors talk us through careful mathematical thinking without ever getting bogged down in equations. Or computer code, for that matter. It's all just nice, clear, readable English.
Am I going to go out now and apply the algorithms discussed here towards improving my own life? Well... maybe. Did I come away from it feeling like I understand the world better? I think so. Did I come away feeling I understand more about the tools we have for understanding the world? Absolutely. I also may have come away regretting more than ever that I didn't major in computer science, because it really did just light up all kinds of nerdy areas in my brain.
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LibraryThing member sstaheli
This book is not what I expected, but it was better! I hadn't thought about many of the concepts in this, or analyzed my logic when it came to making decisions in these types of situations. Many of the theories were described in new ways, and I recommend taking your time reading this to really
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consider the points made. This is not a single-sitting type of read, but it raised interesting questions and insights into my own thinking. Definitely a worthy read...! Show Less
LibraryThing member Katya0133
The highest praise I can give a book is to say it changed my life. This book changed my life before I had even finished reading it.
At the same time, I hesitate to recommend the book broadly because it was such a perfect book for me that I have to assume it would be less perfect for almost anyone
Briefly, "Algorithms to Live By" presents a series of well-known computer algorithms and explains how they are used to solve common problems in computer science. Then it takes each algorithm and applies it to real-world problems, discussing whether or not we, as human beings, approximate the algorithm's behavior with our own actions.
This may make the book sound incredibly dry, like the authors are trying to turn people into computers and squelch out all humanity. Instead, the authors are taking an algorithmic look the human condition. (E.g., how does the explore/exploit trade-off explain why there are certain times in our lives when it's easier or harder for us to make new friends? Can the concept of intractability free us from worrying about making perfect choices?)
If you have a strong background in computer science, you probably won't learn anything new from the algorithms presented in this book, although you may still be interested in some of the real-world applications of those algorithms. However, if you have little background in computer science, but an interest in algorithms and a patience for technical prose—seriously, this is the kind of book where you have to stop and think for a minute between each paragraph—this is the book for you.
You may even find that this book significantly changes the way you approach some aspects of your life, or at least helps you better understand why you behave the way you do.
At the same time, I hesitate to recommend the book broadly because it was such a perfect book for me that I have to assume it would be less perfect for almost anyone
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else.Briefly, "Algorithms to Live By" presents a series of well-known computer algorithms and explains how they are used to solve common problems in computer science. Then it takes each algorithm and applies it to real-world problems, discussing whether or not we, as human beings, approximate the algorithm's behavior with our own actions.
This may make the book sound incredibly dry, like the authors are trying to turn people into computers and squelch out all humanity. Instead, the authors are taking an algorithmic look the human condition. (E.g., how does the explore/exploit trade-off explain why there are certain times in our lives when it's easier or harder for us to make new friends? Can the concept of intractability free us from worrying about making perfect choices?)
If you have a strong background in computer science, you probably won't learn anything new from the algorithms presented in this book, although you may still be interested in some of the real-world applications of those algorithms. However, if you have little background in computer science, but an interest in algorithms and a patience for technical prose—seriously, this is the kind of book where you have to stop and think for a minute between each paragraph—this is the book for you.
You may even find that this book significantly changes the way you approach some aspects of your life, or at least helps you better understand why you behave the way you do.
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LibraryThing member chellerystick
This book was a lot of fun. Its eleven main chapters dip into key computer-science concepts, from sorting and caching to randomness and game theory. Each chapter describes one or more examples of questions or situations you might best address with those concepts. Then the authors also consider
Those with a heavy behavioral science background will find little surprising in those portions of the book, but the concepts and models presented will be things you start seeing everywhere, so they are a great way to reorganize this material and see it afresh.
In describing various aspects of this book to my spouse, who studied a great deal of computer science in his undergraduate work, he exclaimed that the book sounded like it highlighted many key computer science notions. Indeed, we mused whether it might help nontechnical people get some insight into how technical people think, in a "CS appreciation" kind of vein. Certainly anything that can help to bridge that gap could have value in an increasingly technical world.
The book includes no (programming) code and very few mathematical expressions in the main text. Instead, it discusses relatively concrete scenarios and logical thinking, so it should be accessible to college-educated adults. Further details are given in the end notes and references, for those who may want to explore one of the topics more deeply.
You might like this book if you like Steven Johnson, Daniel Levitin, Scott Page, or Peter Pirolli.
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whether humans do, in fact, use these best solutions, and if not, they speculate on why not--are the assumptions of the algorithm not exactly true in real life, is there some evolutionary pressure in another direction, etc.--and how to improve.Those with a heavy behavioral science background will find little surprising in those portions of the book, but the concepts and models presented will be things you start seeing everywhere, so they are a great way to reorganize this material and see it afresh.
In describing various aspects of this book to my spouse, who studied a great deal of computer science in his undergraduate work, he exclaimed that the book sounded like it highlighted many key computer science notions. Indeed, we mused whether it might help nontechnical people get some insight into how technical people think, in a "CS appreciation" kind of vein. Certainly anything that can help to bridge that gap could have value in an increasingly technical world.
The book includes no (programming) code and very few mathematical expressions in the main text. Instead, it discusses relatively concrete scenarios and logical thinking, so it should be accessible to college-educated adults. Further details are given in the end notes and references, for those who may want to explore one of the topics more deeply.
You might like this book if you like Steven Johnson, Daniel Levitin, Scott Page, or Peter Pirolli.
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LibraryThing member jhawn
The computer science of human decisions
LibraryThing member LTietz
I thought Algorithms to Live By: The Computer Science of Human Decisions, was highly readable, pleasantly challenging, and super rewarding. I was somewhat familiar with the logic and computer science concepts, as well as the behavioral science concepts, but it was quite fascinating to explore the
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application of one to another. The author provided a number of excellent examples which were both interesting and helpful. My favorite parts included the author’s discussion of randomness, scheduling, and game theory. I highly recommend this book for anyone who is intellectually curious. Show Less
LibraryThing member BookWallah
Best consumed in short bursts. I was conflicted with this book. I either loved it or I hated it. Sometimes both in the same reading session.
The authors attempt to take the highly specialized world of algorithms, explain them in common terms the college educated should track with, then relate them
In the end I found I really enjoyed reading this book when I took a single chapter at a time, and just focused on the front half of the chapter explaining common algorithms in depth. I fast forwarded through some bits to keep from getting frustrated. so in summary: Best consumed in short bursts.
The authors attempt to take the highly specialized world of algorithms, explain them in common terms the college educated should track with, then relate them
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to modern life, and then make the jump to explaining social behaviors. I would grade those three as A+, C-, and F.In the end I found I really enjoyed reading this book when I took a single chapter at a time, and just focused on the front half of the chapter explaining common algorithms in depth. I fast forwarded through some bits to keep from getting frustrated. so in summary: Best consumed in short bursts.
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LibraryThing member buchowl
Thought provoking on multiple levels.
Decisions, while part and parcel of the human condition, are increasingly being foisted onto computers. In order to accomplish this, the complexity of the decision must be manipulated to allow the machine to come to a "good' decision however "good" has been
It's a good book and I learned a great deal about computers processes. And I did learn a number of interesting insights into human decision making. What I did not appreciate with the book was a subtle implied superiority of computers versus humans. I supposed I shouldn't be surprised as both authors are heavily involved in the artifical intelligence community but I am of the mind that the loss of data required in reversion to the mean/median can often entail the loss of the most interesting data. Plus the restricting of inputs/options which the authors labeled as a computational 'kindness' can be anything but (why does the US presidental election come to mind here?). So while I can most definitely recommend this book to just about anyone it is a recomendation that comes with reservations.
Decisions, while part and parcel of the human condition, are increasingly being foisted onto computers. In order to accomplish this, the complexity of the decision must be manipulated to allow the machine to come to a "good' decision however "good" has been
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defined. Turns out that deconstructing decision making for computers can teach humans a thing or two about decision best practices. Authors Brian Christian and Tom Griffiths apply the problems and solutions of algorithm construction to humans and provide us with a manual of how to streamline some of the messy conundrums we can find ourselves in. It's a good book and I learned a great deal about computers processes. And I did learn a number of interesting insights into human decision making. What I did not appreciate with the book was a subtle implied superiority of computers versus humans. I supposed I shouldn't be surprised as both authors are heavily involved in the artifical intelligence community but I am of the mind that the loss of data required in reversion to the mean/median can often entail the loss of the most interesting data. Plus the restricting of inputs/options which the authors labeled as a computational 'kindness' can be anything but (why does the US presidental election come to mind here?). So while I can most definitely recommend this book to just about anyone it is a recomendation that comes with reservations.
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LibraryThing member kaulsu
I would have given the books higher marks except for two reasons, neither one connected to the other. However, it was worth listening to, and I am glad I did. I do understand many things better for having listened to it!
1. It really was above my pay-grade, though momentarily I always felt it made
2. Christian is either a really boring reader (lecturer?), or the producer of the book did a very poor job on the speed of the recording. I had to speed it up to 1.25% to save my sanity. The voice suppression was a bit distorted, but better than listening to the slo-mo.
1. It really was above my pay-grade, though momentarily I always felt it made
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sense. Not its fault? You are completely correct!2. Christian is either a really boring reader (lecturer?), or the producer of the book did a very poor job on the speed of the recording. I had to speed it up to 1.25% to save my sanity. The voice suppression was a bit distorted, but better than listening to the slo-mo.
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LibraryThing member harmen
Some chapters are interesting enough, but not all. Now I only have to figure out an algorithm to have the best chance of reading the good chapters while not having to read all of them...
LibraryThing member RGaryRasmussen
This is an amazing, albeit unusual self-help book.
Amazing in that it will help you, as long as you possess a bit of intellectual curiosity, to live a happier, more productive life!
Unusual, in that it explores this self-help space from the perspective of computer science!
What the heck does computer
The book's focus is on human decision making: make better decisions, live happier, more productive lives. Simple.
But, how do you make better decisions? Not quite so simple, as there is a lot known and a bit to learn. But, if you are intellectually curious, the learning will be fun, and rewarding. And, you can do it in small steps. No need to digest the entire book before beginning to benefit from it.
So, what is an algorithm? In computer science an algorithm is nothing more than a recipe: a series of steps for a computer to follow to obtain (compute) a desired result.
The genius of this book is that it eliminates the series of steps, and the computer!
If all this sounds perplexing, perhaps an example will help. The first chapter discusses a kind of question we all face: when do I stop looking? When do I stop looking for a better job candidate and hire someone? When do I stop looking for a better parking place, and park? When do I stop looking for a better offer, and sell my house? When do I stop dating and choose a life partner? The answer, sometimes, is after you have evaluated 37% of the possibilities. Add conditions and constraints, many of which are discussed in Chapter 1, and the answer can vary.
This book contains an awful lot of information, and it will take some time to work your way through it the first time. And once won't be enough. You'll want to return to it again, and again, as new life decisions demand careful consideration. Each chapter deals with a kind of decision. As noted, Chapter 1 discusses optimal stopping rules. Chapter 2 discusses when to explore (e.g., try a new restaurant), and when to exploit (go to one you know well). Other chapters deal with: sorting (organizing), caching (what to keep handy), scheduling (what to do first, or last), Bayes rule (how to use data to make a decision), overfitting (when to use less data), relaxation (when to ignore constraints), randomness (when to give chance a say), networking (connecting with others), and game theory (getting into other people's heads).
The book concludes with a call for computational kindness: try to make it easy for others to make decisions.
The more than 50 pages of end notes are an integral part of the book and must not be ignored. Some notes elaborate on the text. Others provide cultural information, or define terms. A few provide computational or technical detail. But, most notes cite references. My one criticism, constructive I hope, would be for the authors to make it much easier for the reader to find the elaborations, definitions, cultural information, and technical information, perhaps by judicious use of footnotes and a glossary, leaving the references to the end notes.
This is a terrific book. It is most highly recommended!
I received a free copy of this book in exchange for an honest review.
Amazing in that it will help you, as long as you possess a bit of intellectual curiosity, to live a happier, more productive life!
Unusual, in that it explores this self-help space from the perspective of computer science!
What the heck does computer
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science have to say about living happier, more productive lives? Quite a bit, as it turns out. The book's focus is on human decision making: make better decisions, live happier, more productive lives. Simple.
But, how do you make better decisions? Not quite so simple, as there is a lot known and a bit to learn. But, if you are intellectually curious, the learning will be fun, and rewarding. And, you can do it in small steps. No need to digest the entire book before beginning to benefit from it.
So, what is an algorithm? In computer science an algorithm is nothing more than a recipe: a series of steps for a computer to follow to obtain (compute) a desired result.
The genius of this book is that it eliminates the series of steps, and the computer!
If all this sounds perplexing, perhaps an example will help. The first chapter discusses a kind of question we all face: when do I stop looking? When do I stop looking for a better job candidate and hire someone? When do I stop looking for a better parking place, and park? When do I stop looking for a better offer, and sell my house? When do I stop dating and choose a life partner? The answer, sometimes, is after you have evaluated 37% of the possibilities. Add conditions and constraints, many of which are discussed in Chapter 1, and the answer can vary.
This book contains an awful lot of information, and it will take some time to work your way through it the first time. And once won't be enough. You'll want to return to it again, and again, as new life decisions demand careful consideration. Each chapter deals with a kind of decision. As noted, Chapter 1 discusses optimal stopping rules. Chapter 2 discusses when to explore (e.g., try a new restaurant), and when to exploit (go to one you know well). Other chapters deal with: sorting (organizing), caching (what to keep handy), scheduling (what to do first, or last), Bayes rule (how to use data to make a decision), overfitting (when to use less data), relaxation (when to ignore constraints), randomness (when to give chance a say), networking (connecting with others), and game theory (getting into other people's heads).
The book concludes with a call for computational kindness: try to make it easy for others to make decisions.
The more than 50 pages of end notes are an integral part of the book and must not be ignored. Some notes elaborate on the text. Others provide cultural information, or define terms. A few provide computational or technical detail. But, most notes cite references. My one criticism, constructive I hope, would be for the authors to make it much easier for the reader to find the elaborations, definitions, cultural information, and technical information, perhaps by judicious use of footnotes and a glossary, leaving the references to the end notes.
This is a terrific book. It is most highly recommended!
I received a free copy of this book in exchange for an honest review.
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LibraryThing member rivkat
Fun exploration of practical statistics/algorithms, used to explain when you should stop searching and how you should guess probabilities when you have almost no information. Discusses various programming concepts and analogizes to ordinary human situations, such as overfitting—when your
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algorithm will perform better on new data when it uses less than a complete set of existing data. Training without sufficient variation can cause similar problems: “In one particularly dramatic case, an officer instinctively grabbed the gun out of the hands of an assailant and then instinctively handed it right back—just as he had done time and again with his trainers in practice.” Conversational feedback, where you respond to your audience’s signals, has similarities to responses in packet sending, and I liked the idea that human interpretation can provide robustness to voice commuication; robust protocols are less necessary when people can say “hey, say that again.” Best tidbit: “Religion seems like the kind of thing a computer scientist rarely talks about; in fact, it’s literally the subject of a book called Things a Computer Scientist Rarely Talks About” (in the context of a discussion of how shared norms/constraints can solve prisoner’s dilemmas and improve outcomes for everyone even when they look like they’re worsening the choices available to the individual). Show Less
LibraryThing member reenum
This book is a very good introduction to several mathematical concepts that many people have heard of, but don't know much about. Brian Christian also does something very clever: he makes these concepts eminently relatable.
It may sound like hyperbole, but the chapters on optimal stopping and
The most impactful concepts are clustered in the front of the book, which is again optimal for those readers with short attention spans. The stuff later in the book is also very enlightening, just not as universally applicable as optimal stopping or explore/exploit.
This is not a book designed for people with an advanced understanding of math or computer science. It's designed as a gateway to bring in people like me who are interested in these fields, but are perhaps a little intimidated.
Read it now, people.
It may sound like hyperbole, but the chapters on optimal stopping and
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explore/exploit changed my life. I save a lot more time not trying to figure out which parking spot to choose or where to eat.The most impactful concepts are clustered in the front of the book, which is again optimal for those readers with short attention spans. The stuff later in the book is also very enlightening, just not as universally applicable as optimal stopping or explore/exploit.
This is not a book designed for people with an advanced understanding of math or computer science. It's designed as a gateway to bring in people like me who are interested in these fields, but are perhaps a little intimidated.
Read it now, people.
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LibraryThing member nmarun
This book provides deceptively simple insights into the day-to-day chores that we do. I don't think I'll every sort my bookshelf, as scanning it is lot more easier. The book provides some examples on how to implement some of these algorithms in our daily routine.
I knew some of the concepts that the
I believe this book is mostly for Science and Mathematics enthusiasts. A casual reader might get lost in the deep computer- and statistics-theories provided throughout the book. Being one such person, I thoroughly enjoyed reading this book.
I knew some of the concepts that the
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book mentions, but algorithms like Optimal Stopping Theory and Bayes' Theorem were definitely eye-catching and thought-provoking. The chapter on Networking discloses complex TCP concepts in relatively easy language.I believe this book is mostly for Science and Mathematics enthusiasts. A casual reader might get lost in the deep computer- and statistics-theories provided throughout the book. Being one such person, I thoroughly enjoyed reading this book.
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LibraryThing member superpatron
Subtitle "The computer science of hard decisions"
It seems at the face of it very simplistic, and my thesis from a quick read is that it suffers from a fundamental fallacy. Computers are immortal, and don't suffer existential problems where if they make a bad decision they don't get to make
It seems at the face of it very simplistic, and my thesis from a quick read is that it suffers from a fundamental fallacy. Computers are immortal, and don't suffer existential problems where if they make a bad decision they don't get to make
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decisions any more. Humans are mortal and have to somehow decide in the presence of decisions that may be catastrophic. Show Less
LibraryThing member andycyca
A very interesting primer on the sort of "real-life problems" that have parallels with computer science problems, explained elegantly without numbers and formulae, opting for the better approach of explaining the rationale behind the problems, their assumptions and how they lead to a desired
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output. Highly recommended for everyone looking to improve in practical matters, this book throws in a good number of questions to ponder about in our lives, expectations and how we go about reaching them Show Less
LibraryThing member haraldgroven
Ingenious book about how many human decision problems are in fact algorithmic, i.e. the kind of problems researched in computer science. The authors structure the book around classes of algorithms (eg. sorting, optimal stop, regularization, network flow, bayesian stats or game theory) and find
You won't learn any math, programming or even pseudo-coding from "Algorithms to live by", but it will give most readers a broad range of examples of how computer science is not just about computers, but the study of complexity and how to do decision making using rules (which may be automated).
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interesting anecdotes or every day life examples of each algorithm. You won't learn any math, programming or even pseudo-coding from "Algorithms to live by", but it will give most readers a broad range of examples of how computer science is not just about computers, but the study of complexity and how to do decision making using rules (which may be automated).
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LibraryThing member antao
Regardless of what Feynman said (it wouldn't matter if even Gödel said it), Computer Science is not engineering. I add some caveats to that, Computer Science is not so easily categorized, so by Computer Science I refer to the formal science aspect. Now, referring to "Nature", two things, some
Some of Computer Science that is. This is where the murkiness comes in because it also investigates many other things. Some Computer Scientists work on logic, and as yet whether logic is normative or descriptive is one of those big questions that is currently left to philosophy. What about Programming Language design? Well, formal work in this area does involve proving theorems, but about human artefacts. We can further cloud the picture with inter-disciplinary work. Computer Science used to discover how fish coordinate their behavior, for example.
But, removing the mud, we have left theoretical Computer Science and that is really the easiest thing to defend as Science. By which I mean, decidability, complexity, etc. I have already pointed out this says something about the universe, I think the main attack would be "how can saying something about computers be saying anything about nature when they are human-made artefacts?". The thing is, we are really talking about computation and not computers, computation is of course a human concept but it conceptualizes something real in nature. We can make other conceptualisations of course, but so long as there is a morphism between them and nature, anything we say about them is also said about the universe around us.
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results of Computer Science if they were different we would expect the universe to be fundamentally different. A predictable argument from me, but result still to come is P vs NP - if we have P = NP then I think we would all agree that the universe is not quite as we thought it was. Computer Science does say something about Nature.Some of Computer Science that is. This is where the murkiness comes in because it also investigates many other things. Some Computer Scientists work on logic, and as yet whether logic is normative or descriptive is one of those big questions that is currently left to philosophy. What about Programming Language design? Well, formal work in this area does involve proving theorems, but about human artefacts. We can further cloud the picture with inter-disciplinary work. Computer Science used to discover how fish coordinate their behavior, for example.
But, removing the mud, we have left theoretical Computer Science and that is really the easiest thing to defend as Science. By which I mean, decidability, complexity, etc. I have already pointed out this says something about the universe, I think the main attack would be "how can saying something about computers be saying anything about nature when they are human-made artefacts?". The thing is, we are really talking about computation and not computers, computation is of course a human concept but it conceptualizes something real in nature. We can make other conceptualisations of course, but so long as there is a morphism between them and nature, anything we say about them is also said about the universe around us.
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LibraryThing member Paul_S
I knew about most of these algorithms and theorems already but the book goes beyond that and shows how they apply outside abstract problems. Thought stimulating and well written to boot, in a friendly and informal manner but without cramming in forced humour (my pet peeve in pop-science books).
LibraryThing member bangerlm
This book is very much in my wheel house--computer science and interdisciplinary analysis, and I really enjoyed it. I think it makes for a lot of interesting conversation. This may become 5 stars if it sticks with me.
I really liked the ideas about how our seemingly irrational behaviors are often
My one critique was the section about pecking orders and dominance hierarchies, while yes there are observations in the natural world, and that can be related to sorting, making arguments that hierarchies are "scientifically" better or more peaceful is strongly akin to past scientific rationalization that have been used for centuries to justify racism, sexism, etc. It is better to just not go down that road.
I really liked the ideas about how our seemingly irrational behaviors are often
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due the fact that the problems are intractable and thus are rational, how the fact that it takes longer to remember things might not be degeneration, but a natural consequence of searching massive amounts of data, that piles are not a disgraceful form of organization, overfitting (I am personally angered by how this happens in our education system), computational kindness, and how exponential backoff "offers a way to have finite patience and infinite mercy."My one critique was the section about pecking orders and dominance hierarchies, while yes there are observations in the natural world, and that can be related to sorting, making arguments that hierarchies are "scientifically" better or more peaceful is strongly akin to past scientific rationalization that have been used for centuries to justify racism, sexism, etc. It is better to just not go down that road.
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LibraryThing member datrappert
Lots of interesting stuff in this engaging look at how we might apply computer algorithms to daily life. Unfortunately, many of the algorithms are for solving problems that are much simplified compared to their human equivalents. And unsimplifying them sometimes leads to problems that are basically
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unsolvable. This is where some of the book's most interesting sections occur, however, such as the revelation that sometimes less data can produce a better forecast than more data, or that in some cases a high probability can substitute for mathematical certainty. Sometimes, the authors don't do justice to an algorithm, however. The Vickrey Auction (where the winning bidder pays the amount bid by the second highest bidder), for instance, is presented as being almost infallible, but a quick Google search seems to show that it results in overbidding. Whereas in most cases throughout the book, a reader will say, "Wait a minute, it doesn't work like that" and a few pages later the authors address that concern or a similar one, in this case it appears the authors were rushing through the last chapter on Game Theory and just wanted to be done with it. Nevertheless, I recommend this to anyone who has an interest in decision making, how humans think, and how computers think. It is an eye-opening and mind-opening read. Show Less
LibraryThing member kokeyama
The authors review tried and true algorithms from the fields of mathematics and computer science and show how they can be applied to real world decisions that you and I may make every day. Not only is it a great demonstration of real-world applications, the book is quite fun to read.
Language
Original publication date
2017
ISBN
9781627790369
DDC/MDS
| 153.43 |
Other editions
Algorithms to Live By by Brian Christian (Ebook)