In Algorithms to Live By, each chapter focuses on one type of problem that human people face in their lives. The first chapter, for example, focuses on "optimal stopping," the problem of knowing when to settle for something, even if something better could be just around the corner. Specifically, the setup is this: you have a bunch of opportunities—jobs to work, houses to buy, days to hunt for an apartment, whatever—and when given an opportunity, you can either commit to it or abandon it forever.
The trouble is, you don't know how good all of the opportunities are, and the only time you're introduced to an opportunity is when you're being given the choice to take it or leave it. Going for the first one seems foolish– after all, there's probably a better opportunity out there. But waiting until the last one is pointless– sure, now you know how good all of your options were, but at this point you don't really have a choice in the matter, so you just have to hope the last one is good. What, then, is the best thing to do?
This question does actually have a mathematically correct answer: First, estimate the number of opportunities you will have– jobs that will consider you, houses that you will be able to afford, time available to spend looking for an apartment. Then, multiply that number by 37%. Use those first 37% of opportunities to calibrate your expectations; don't commit to any of them. After that 37%, go ahead and commit to the next opportunity that is better than all the ones you've seen so far.
This strategy will get you the best possible choice, out of all of the seen or unseen options, 37% of the time. Which, when you think about it, is kinda crazy. You start the process knowing nothing, and at each step you only have one choice: commit or press on. And yet, almost two in five times, you'll be able to come out of it just as well as if you'd already known everything when you'd started.
The chapter then goes into possible extensions of the problem: what if there's a cost to waiting longer, or what if you have an idea of what the average opportunity looks like? These slight changes to the problem can often change what the best solution is, and they each have their nuances and complications. Other chapters have similarly broad scopes, and cover topics like how best to tidy things up, how to decide what's worth pursuing, and how in general to deal with other human persons. Each chapter feels like its own attempt to optimize some major facet of life that you never even knew could be optimized.
One of the reasons I like Algorithms to Live By is that it is very matter-of-fact and not at all judgemental. "Yes, this is a problem." "No, the solution is not obvious, which is why it's a problem." It goes into a lot of examples, and often waxes philosophical, but in the end it's really a book about solving problems and being a better human person. Even when there is no good solution, it can be nice just to know that the kinds of decisions you face are sometimes ones that, in a provably true way, have no simple answer. Algorithms to Live By has really boosted my confidence in my own decision making, and I think made my life better as a whole.
Note that I might be biased towards the book in that I am in fact a mathematician, and the idea of something being proven holds a lot of power over me. But, to be clear, this is in no way a math book, nor does it require any mathematical knowledge beyond maybe knowing what a percentage is. I, myself, have no experience with computer science at all, but Algorithms to Live By was still incredibly easy to read and understand.
So if you are currently or have ever been a human person, and would like to maybe be a bit better at doing human person things, then you should pick up Algorithms to Live By. It's interesting, easy to read, and all in all a good way to optimize your human person experience.
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