Algorithms to Live By: The Computer Science of Human Decisions 1st Edition by Brian Christian (PDF)

Description

An exploration of how computer algorithms can be applied to our everyday lives to solve common decision-making problems and illuminate the workings of the human mind. What should we do, or leave undone, in a day or a lifetime? How much messiness should we accept? What balance of the new and familiar is the most fulfilling? These may seem like uniquely human quandaries, but they are not. Computers, like us, confront limited space and time, so computer scientists have been grappling with similar problems for decades. And the solutions they’ve found have much to teach us.In a dazzlingly interdisciplinary work, Brian Christian and Tom Griffiths show how algorithms developed for computers 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 peering into the future, Algorithms to Live By transforms the wisdom of computer science into strategies for human living.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐Within the “Scheduling” chapter they indicate that “of the 93% of the problems that we do understand… only 9% can be solved efficiently…” This appears to apply to much of the book regarding the various topics covered. Often the treaties are interesting, but the solutions are often impractical, inapplicable, or outdated.Within the “Caching” chapter the authors make much about human memory and Media headlines both fading away very rapidly as time goes by. They feel this forgetting pattern is part of an underlying universal principle. It may be, but when you look at their own graphs (pg. 101) on the subject they omit to emphasize that the graphs have completely different scale on the x-axis. What human beings forget in a matter of hours, the Media moves on in a matter of days. It is a very different time scale that diminishes the insight associated with this principle besides the obvious: yes indeed individuals and even societies have a limited memory (on their own different respective scale).They are often not in tune with the information age. For instance the algorithm that dominates the first half of the book is the 37% rule that you should stop gathering data regarding decisions after researching 37% of the data you were considering exploring. This virtually applies to everything. If you were planning to date 10 different people before getting married in order to “shop around” you apparently have enough info after dating the first 4. If you are planning to rent an apartment the same is true (you have enough info to make a good choice after interviewing the first 4 applicants out of 10). If you are planning to sell a house you can accept an offer after passing on the first few of them. If you are recruiting and hiring a secretary the same principle holds up.However, with the online world we have so much more information than the world the author describes. Regarding mating with numerous online websites one has so much more information and choices than what the 37% rule would suggest. The same is true if you are hiring a secretary, you can just advertise on an online platform receive a 100 resumes in a few days. Filter those resumes, interview just a few candidates, select the best one and be done with it. This recruiting renders the 37% rule irrelevant (you don’t need to interview 37 candidates out of 100 since you already have a lot of info on all of them before interviewing them).Also, absent from the math the authors convey is the concept of supply and demand. When selling a house, this transaction is dominated by the local supply and demand. For instance, anyone who has sold a home during the housing crisis most probably did not have the luxury to wait out for better offers such as the 37% rule would suggest. In general, waiting for a better offer does not work well in real estate. A house number of days on the market is a measure of how stale a prospective home sale gets. Waiting for better offers (37% rule) typically does not work. That is why sellers remove their homes from the market to give them a fresh reset.Also absent from the authors’ calculations are moral considerations as they state: “if you are a skilled burglar and have a 90% chance of pulling off each robbery (and a 10% chance of losing it all [by being caught] ), then retire after 90/10 = 9 robberies. Cool math but not exactly “Algorithms to Live By” as the title suggests.On other occasions, they do not support or explain the underlying math at all. Such is the case for the Gittin Index they cover on page 39 to 42. The latter is associated with counterintuitive results that remain confounding.Other algorithms appear flawed. This includes the Upper Confidence Bound algorithm that supposedly guarantees minimal regret. I am unclear how that would be the case because by selecting such an option you also take the maximum risk. That’s what condo flippers did during the housing crisis Leveraging gets you up on the Upper Confidence Bound… but also the Lower one.The authors cover the most important subject Bayesian statistics within chapter 6. However, their treatment of the subject focuses a lot more into challenging technical considerations like the probability distribution of the a priori events (Normal, Power, Erlang, etc.) rather than on explaining the basics of Bayes theorem. Without establishing a good foundation explaining Bayes theorem any insights regarding a priori events distributions are rather obfuscating. For a better coverage of Bayesian statistics Nate Silver’s “The Signal and the Noise” is a lot more edifying.Several of their chapters’ subjects and titles use confusing play on words that make them sound like they are relevant to your daily life but they really are not. The chapter on “Relaxation” has nothing to do with relaxation. It describes mathematicians removing technical math constraints from very challenging problems in order to being able to solve them. The chapter on “Randomness” has also little to do with a layperson’s meaning of randomness. Instead, it deals with technical math concepts regarding sampling, Monte Carlo simulation, and randomized algorithms. Those represent another set of math strategies to solve what would be otherwise unresolvable problems.The book is not all bad.The chapter on “Overfitting” is excellent. That’s even though it is still aimed at the math geek crowd and provides little in terms of “Algorithms to Live By.” This book is truly very mistitled and mispecified in terms of audience target. In this chapter, they warn against developing models associated with higher degree polynomials to better fit the curve of a given data set. This is not just with higher degree polynomials but often any model that has a lot of variables that fit the history of the data really well. Such complex models with many variables often do a worse job of predicting given new data vs. much simpler models that do not fit the learning sample of the model as well. Their referring to cross-validation to test for overfitting, regularization to preempt overfitting, and stepwise methods to build streamline models consists of interesting arcane math technicalities. None of them have much relevance in your daily life decisions.The chapter on “Game Theory” is also excellent. Their treatment of Game Theory is very good. Additionally, their explanation of a specific Game Theory situation: Information Cascade is truly fascinating and for once most relevant. It explains a whole lot about group behavior, asset bubbles, and related financial crises. What others have often described as the “madness of crowds” to explain bubbles may be better explained by information cascades. During the most recent financial crisis, each relevant party may have followed their own rational economic interest. But, the whole economic sector was plagued by negative equilibria that lead to inevitable disasters. This is a characteristic of information cascades as described within the book in the section “Information Cascades: The Tragic Rationality of Bubbles.”My rating reflects that there are only two excellent chapters out of 11, and most of the math content is not really relevant to your daily life. If you have not heard of the 37% rule, there is a good reason for that; it is obsolete.

⭐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.

⭐Using what appears to be trivialities, what this book does is gently steer you towards achieving and leading a worthwhile fulfilling life by using AI (Artificial Intelligence) logic to help you to spot and understand hidden underlying, severely flawed, deeply entrenched, ingrained systems and practices that are out there to deliberately ensnare you; trap you; and ultimately enslave you and destroy you should you fail to grasp the nettle and do something about the root causes of your unhappiness – be it staying in a pointless fruitless job; failing to acquire skills you need to advance yourself; being influenced by others; or enduring a worthless unfulfilling relationship.You are then given guidance to develop strategies for living in happiness by using a more LOGICAL approach to spot danger and take positive action to prevent jeopardy by considering if what you are doing is meaningful and worthwhile and brings LONG-TERM happiness.This ‘ME TOO’ book is not for everyone because it asks you to examine and challenge traditional ‘taboos’ and what is euphemistically known as ‘conventional wisdom’ – and then having the COURAGE to take the required actions to set your life in order and gain your liberty and FREEDOM.Five stars

⭐I am not a computer scientist but a statistics student. Obviously very well researched. The best thing about it is its relevance to real world problems and you come away with useful knowledge to apply. There is no maths in the text but instead the rational logical format expected from computer scientists. This means that it doesn’t flow easily and you have to concentrate at times to keep the thread hence the mark-down. This book raises a bigger point for me: computers are logical because they were built and coded by us so really they are just versions of us with bigger brains. Which means many of the point raised surely are just things we would have concluded by ourselves if we had the time & patience to crank the computations, right?

⭐This book does several things very well indeed. It introduced a broad range of Computer Science’s fundamental algorithms, explaining them simply and clearly. It shows how we might apply these algorithms in our everyday lives, to help us make more efficient and effective decisions. And it shows that even when we have the provably best means of making a decision, it might not always (or even very often) work.It covers approaches to searching, and when to stop looking for improvements over what you already have. It discuses sorting, and tradeoffs between time spent keeping things in order, and time spent finding them later. It covers scheduling, and how the best order to do things in depends very much on what you are trying to optimise. It finishes with game theory, explaining why some situations lead to poor outcomes for all, and how understanding this can help you know how to change the situation to get better outcomes. And it does all this, and more, with a light touch that makes it very readable.

⭐This book talks about the history and evolution of algorithms from the very beginning, talking about particular problems and the different approaches scientists, mathematicians and others have adopted in trying to solve these issues. This book is clever in how it can get the reader to see a general problem and showing them how it can be broken down into different categories that a computer can solve, and how the thinking to solve that problems can solve other problems. This is a key skill to have in fields such as applied mathematics and computer science and gets the reader thinking about problems in the right way.

⭐This book is a well-made translation of the algorithmic thinking used by computer scientists into plain-English. I like the author’s style of writing because it is straight to the point and accessible for laypeople. He make hard concepts easy to understand and uses a lot of examples throughout the book. Amazing piece of work!

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