Algorithms to Live By

I picked up this book at the recommendation of a friend, and as someone who studied Computer Science at university, I wanted to love it. I did find this book interesting, but I also found myself skimming a lot. I felt that some of the chapters were quite long and I lost interest towards the end. I'm not sure it got the right balance between hard CS concepts and their real-life applications.

As a non cs person, this was interesting to learn about psychology through the lens of computer science and logic. Also a lot of principles which are applicable to daily life if you like overthinking things LOL.

Algorithms To Live By: The Computer Science Of Human Decisions is a book written by Brian Christian and Tom Griffiths, that tries to compare human decision making with algorithms developed by engineers for the efficient running of computer systems. The books points out several daily life cases where algorithmic logic can be employed for decision making. Algorithms are basically a series of steps that solve a problem, which is what we do in our life too, sometimes consciously, like when we assemble a furniture using instructions or many times unconsciously, like when we plan a shopping trip across multiple shops.

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Interesting overview of real life issues and how they were (or weren't) solved by various research areas in computer science/algorithms. Fantastic chapter on game theory and its applications, but quite a few other ideas will stick with me for sure.

I picked up this book at the recommendation of a friend, and as someone who studied Computer Science at university, I wanted to love it. I did find this book interesting, but I also found myself skimming a lot. I felt that some of the chapters were quite long and I lost interest towards the end. I'm not sure it got the right balance between hard CS concepts and their real-life applications.

I enjoyed the first chapter and the chapter on distribution curves (standard, exponential, Erlang) and corresponding prediction models.

Much of the rest of the book was frustrating to read. It tries to cover so many topics so thinly that it barely gets into one observation before it leaves it behind for another.

On page 74 it starts into an interesting discussion of sports tournaments and mentions Lewis Carroll's pamphlet on lawn tennis brackets. But then, in less than a page, it skips over Carroll's proposed solution (because it's an “awkward take”).

Or the page on unlimited vacation time for employees (which features a quote from a friend of mine) barely addresses a single objection and then moves on to other topics.

Instead of introducing readers to the complexity and nuances of the difficult and often decades-long dilemmas of computer science, it turns them all into misleading single page summaries.

I would have preferred to read a book where they picked half as many topics and discussed them more thoroughly.

Reading this book confirmed a new personal approach to avoiding books that will be disappointing:

- About 250 pages long
- Subject is vaguely scientific or mathematical
- Uses the Gotham font or another geometric sans anywhere

This book satisfies all three and now I know that I should avoid buying this kind of book.

I listened to this as an audiobook which led to listening to a lot of cyclical chatter. In general, I appreciated how it took a different lens to everyday occurrences.

Interesting read. The discussion of how some CS algorithms mirror behaviors seen in nature and how some CS algorithms have influenced solutions in a non-CS context was interested. In some cases material seemed to be there as filler (a general discussion of an algorithm without really explaining how it had anything to do with non-CS things), and in a few cases things were stated in a way that was oversimplified (given the context). All in all, a good, light read.

Algorithms are recipes and strategies, and whenever we have to make decisions in real life - influenced by a set of restrictions on time/money/space - we apply our own internal algorithms. Sometimes our techniques have grown from years of experience, sometimes we can explain their reasoning, sometimes we refer to it as a gut feeling. Mostly, impressively, there are not too far from how an optimised computer algorithm would solve the same problem. Algorithms to Live By goes through daily-life examples and explains the probabilities and math behind such decision-making problems. How to find a close-enough parking spot without wasting time circling the block. When to explore new restaurant options instead of returning to favorites. How the messiest desk of piles of paper actually resembles the most efficient last-in-last-out caching strategy. Some have magical numbers attached to them (stop exploring after 37% of your options and exploit the next best option), others are well-known principles (like ‘perfect' being the enemy of ‘good'). The book is a good companion to [b:How Not to Be Wrong: The Power of Mathematical Thinking 18693884 How Not to Be Wrong The Power of Mathematical Thinking Jordan Ellenberg https://images.gr-assets.com/books/1387726285s/18693884.jpg 26542434], as both try to coach us into understanding the mathematical parts of our lives a bit more.

Interesting but only intermittently useful. Most of the scenarios they set up are not truly real life solutions (or indeed problems).

This book was pretty thought provoking in its discussion of how we can apply computer science to human problems. The best part of this book is its beginning, with its exposition of the Optimal Stopping problem and exploration/exploitation tradeoffs. I had some political grumbles about some of the authors' choices of examples, but that's not directly relevant to the material so I'll keep my mouth shut.

A book for maths & computer science algorithm geeks. No practical value beyond that I misunderstood what it had to offer