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Algorithms to Live by

Algorithms to Live by

The Computer Science of Human Decisions

Book - 2016
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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.
Publisher: New York : Henry Holt and Company, 2016
Edition: First U.S. edition, first International edition
ISBN: 9781627790369
Call Number: 153.43 C4624a
Characteristics: 351 pages : illustrations ; 25 cm
Additional Contributors: Griffiths, Tom 1978-- Author


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Jan 30, 2021

As a puzzle creator, this book was very appealing to me. It's full of optimization-type problems, which are categorized based on the type - like sorting, caching, sampling etc. And thus, it almost reads as a layman version of Analysis and Design of Algorithms textbook or Cracking the Coding Interview! In addition, the book carries a lot of trivia and history of many classic problems. Did you know the first code ever written for a “stored program” computer was a program for efficient sorting? Or, that Horace Walpole coined the term “serendipity”, based on the fairy tale adventures of The Three Princes of Serendip (Serendip being the archaic name of Sri Lanka). Or that Jon von Neumann invented MergeSort? And it also carries another type of interesting analysis based on the described algorithms - did you know Egyptian pharaohs’ reigns follow an Erlang distribution?

But what surprised me the most is that it carries a chapter on Machine Learning, and accurately describes a lot of ML concepts like overfitting, regularization etc using simple analogies! The explanation of overfitting might answer a common question we all might have - how can it be that the foods that taste best to us are broadly considered to be bad for our health, when the entire function of taste buds, evolutionarily speaking ,is to prevent us from eating things that are bad? Or the question of why organisms aren't "perfect" to their current environment.

As expected, if you enjoy nerdy jokes, there's a plethora of them, and most of them from real incidents. A few of them include: regarding the impossibly tough "optimization class" problems, it was said that they so sapped the energies and minds of Allied analysts that the suggestion was made that the problem be dropped over Germany , as the ultimate instrument of intellectual sabotage; Google's ad gurr Siroker's claim that the best minds of his generation are thinking about how to make people click ads etc. But the best of all is clearly this: The comedian Mitch Hedberg recounts a time when “I was at a casino, I was minding my own business, and this guy came up and said , ‘You’re gonna have to move, you’re blocking the fire exit.’ As though if there was a fire, I wasn’t gonna run.” The bouncer’s argument was priority inversion. You can never block a fire exit if you can run!

Other than these, the book obviously has a myriad of interesting problems and solutions, along with their applications to real life scenarios, mostly in optimization and exploitation class of problems. Some of them include why Hollywood is obsessed with sequels (it's Mathematically lucrative and logical); Gittins Index; Jeff Bezos' Regret Minimization Framework; A/B testing of buttons by Google; Tuskegee's Syphilis Study based on A/B Testing principles; the question of socks on StackOverflow; Why is the grass greener on the other side? (Yes, there's a Mathematical explanation!); why 37% is the optimal number for all exploration problems? (after exploring how many apartments should you settle on one?) etc.

Overall, this was a fascinating read. I'd keep this as a reference book and hope to create an online wiki of such optimization problems!

Oct 21, 2020

Excellent lessons about algorithms, life, and how they interact. I recommend this book to anyone curious about the title.

Apr 29, 2019

In this book the authors explain famous algorithms in real world context.

My notes from this book -

(1) Optimal Stopping
(2) Old people don't lose memory - they have so much of it that it slows their system.
(3) Procrastination can be seen as an efficient scheduling problem with wrong priority.
(4) Predictive Models - Gaussian, Power Law, Erlang
(5) Over-fitting - "It really is true that a company will build whatever the CEO decides to measure".
(6) Penalize complexity - Occam's Razor Principle
(7) "A bit of conservative, a certain bias in favor of history, can buffer us against the boom and bust cycle of fads"

(8)Over-fitting Examples - Military Training, taste buds
(9) Early Stopping - Appropriate for Uncertainty
(10) "The prefect is the enemy of the good."
(11) Continuous Relaxation for discrete optimization.
(12) Lagrangian Relaxation - "You don't HAVE to obey the law. There are consequences to everything and you get to decide whether you want to face those.

(13) Random Sampling - Miller Rabin Primality Test
(14) Charity - GiveDirectly uses random samples of review
(15) Bloom filters for search engine crawls.
(16) Simulated Annealing - Random restart hill climbing.
(17) Randomness - heart of creativity?
(18) Networking - Circuit Switching -> Packet Switching
(19) Exponential backoff
(20) AIMD - Additive Increase Multiplicative Decrease, TCP's Sawtooth
(21) Game Theory - Price of Anarchy. Selfish routing only has 4/3 as it's price of Anarchy that's how internet is working fine (infact 33% close to optimal).

(22) Price of Anarchy is very high in case of Prisoner's Dilemma.
(23) Tragedy of Commons - Pollution, Climate Change, Number of Vacations employees use etc.,
(24) Game Theory - Information Cascade.
(25) Vickrey Auction

I recommend this book to all!

Nov 10, 2017

Algorythms to Live By, was like learning to make life decisions according to calculated odds as per sociologists. Life decisions are more than actuarial works. Didn't care for the mathematical formula of life. Logic is fine. But life is also about reasoning & Intuition with a capital I. Odds should be only for bookies, actuaries & assorted risk takers & gamblers.

Apr 18, 2017

I installed the cloud library application on my computer(windows 10), I can sign in and see the books which I borrowed. But it crashed when I tried to open it. so, no comment for this book.


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