Algorithms to Live By – The Computer Science of Human Decisions

That is what I do ...I stop reading at 37% of the comments :-)

TL;DR: this works in a scenario where you must immediately choose one from a series of randomly ordered candidates of a known quantity. To optimize the probability that your choice is the best candidate:

1. reject the first ≈ 37% of candidates; 2. choose the subsequent candidate that is better then any seen so far.

Isn't that just using the subconscious's algorithms instead of the conscious's?

The Alignment Problem was a stand out read for me this year; it should be required reading for anyone training and deploying ML models. Incredibly well researched and chockablock with real world examples.

I also recommend other books by Brian Christan, especially the Alignment Problem.

It's not about optimal matches at all - it's about when to stop looking.

The assumption is you don't known the set of potential matches, or the order they come in, or anything really. But there is a deadline for the decision (or a maximum number of attempts). So how to balance making attempts to gather information with committing to a final decision so you don't run out of time? All else being equal, the rule is 1/e. Spend the first 37% of your time/attempts gathering information, then commit to the next option that's better than you've seen.

This doesn't guarantee a good match (or even a match!) but probabilistically the strategy is optimal.

I think they address this in a discussion about lacking full information.

"We don't have an objective or preexisting sense of what makes for a good or bad applicant; moreover, when we compare two of them we know which of the two is better, but not by how much." (p. 18)

They then go on to explain stopping thresholds in the cases when you do have full information.

My understanding of the optimal stopping problem is that you basically take a sample that's approximately N/e, and then you accept the first candidate that exceeds the scores of the members in that sample, or you accept the last candidate, regardless of their score.

I think the key thing is that a priori you don't know what the spectrum of quality is in the initial set. You're basing "the bar" on what you've just seen. It's like dating, you might pass on some great people before you realize what the dating pool really looks like.

I read the book a while back and realized I do the caching one automatically. I have a pretty messy work bench where I build rockets and play around with microcontrollers. I purposely didn’t try to organize it because, over time, it organizes itself. All the stuff that has my attention gradually drifts to arms reach where the stuff I don’t currently need gradually drifts to the back of the workbench.

Edit: the stopping and explore/exploit chapters mirror my career too

I apply some things from the explore/exploit chapter when travelling. If for the first half of the trip I try as many places to eat as I can. For the second half I’m fine with revisiting the best ones.

I used the optimal stopping guidelines to help people I mentor stop applying for jobs and change their resumes/approach. It worked pretty well for them.

I read the book around 3-4 years ago and regularly returning to the exploit vs explore and randomness concepts. 37% rule is something I regularly talk about as well, but mostly to help other people make sense of the dilemmas "should I continue looking or stop now" (like searching for a flat, for example).

I often make estimations based on the heuristic that if we don’t know much about how long something will remain, then we’re most likely half way currently. For example, McDonalds was founded 82 years ago and if we have to guess how long it will still exist then probably around 82 years (until 2104).

This also works great, for example, to answer whether you should make plans for Christmas 2023 with the girl you have been seeing for two months now: probably not yet.

I’ve read years ago, and became my comfortable keeping my inbox or my files become messier, relying more on the search. I wish Google Desktop would still exist, however.

When dealing with particularly toxic people I find the exponential backoff to be an excellent strategy.

In my case, I hate cutting people off because I know people can change. What I do to manage relationships is run a forgiving version of exponential backoff. Start off friendly and forgiving. If someone becomes transgressive, increase the latency between interactions. If the transgressions continue, double the latency. If bad interactions persist, the time latency can go on to months or even years which means you'll probably never interact with that person again. Conversely, if an interaction goes well, reduce the delay for when you're willing to meet again. E.g. say an irritating individual causes the latency to go to once a month. If you have an interaction that goes well then the latency drops to 2 weeks. If interactions continue to go well they drop further to say no latency, i.e. you're willing to meet this person whenever. Obviously it's not perfect but it suites my needs quite well.

I also found his chapter on "overfitting" excellent. I like to think of it as "smart person disease." Big idea is that having more data can actually hamper decision making instead of enhance it because you winde up solving the wrong problem.

I am currently 35% the way through of all my Hinge matches, planning on proposing to the next girl I grab drinks with.

After reading the book, I use the exponential backoff approach to relationships that seem one-sided.

If you read it, you'll find it full of useful strategies to leverage in making better decisions in your life. It's also amusing for CS-educated folks because it's a fun application of the material to everyday life.

This isn't a self-help book, so your whole big rant misses the mark.