Just want to give a quick shout out to coauthor Tom Griffiths for being an amazing educator; I attended his class before this book was published and was delighted when this book covered the general ideas covered. I’m always happy to recommend it to others looking to understand more about computer science in an approachable way
Algorithms to Live By – The Computer Science of Human Decisions
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Re: Algorithms to Live By – The Computer Science of Human Decisions
#22For anyone who's curious, the 37% for optimal stopping is the rounding of 1/e. https://en.wikipedia.org/wiki/Secretary_problem
Re: Algorithms to Live By – The Computer Science of Human Decisions
#23> Other animal behavior also evokes TCP flow control, with its characteristic sawtooth. Squirrels and pigeons going after human food scraps will creep forward a step at a time, occasionally leap back, then steadily creep forward again. > Caching gives us the language to understand what’s happening. We say “brain fart” when we should really say “cache miss”. Sorry, but how can anyone find this book insightful? Doesn't…
This isn't a self-help book, so your whole big rant misses the mark.
Re: Algorithms to Live By – The Computer Science of Human Decisions
#24This claims to be a review, but is mainly just a summary / rehashing of the content. Feels a bit disingenuous.
I can warmly recommend the book, though.
Re: Algorithms to Live By – The Computer Science of Human Decisions
#25This is a really insightful book. I read it as part of a book club. The algorithm that generated the most delightful discussion and examples was the optimal stopping algorithm.
Re: Algorithms to Live By – The Computer Science of Human Decisions
#26In the section on over-fitting:
"[…] focusing on production metrics led supervisors to neglect maintenance and repairs, setting up future catastrophe. Such problems can’t simply be dismissed as a failure to achieve management goals. Rather, they are the opposite: The ruthless and clever optimization of the wrong thing."
Southwest Airlines.
Re: Algorithms to Live By – The Computer Science of Human Decisions
#27Sounds like the author is slowly discovering micro economics?
Re: Algorithms to Live By – The Computer Science of Human Decisions
#28Optimal Stopping Problem always confused me, since it seems to assume you’re not aware of a meaningful measure of what an optimal match would be, but aware of what the optimal set of potential matches is.
For example, say there’s a goose looking for a mate and they only look at geese of the opposite sex, but in fact, that specific goose’s optimal mate type is a black swan. Maybe it’s just me, but at the point you’re able to limit yourself to a type of X then you likely known Y are the attributes that best define it.
Am I missing something other than the obvious point that as the selector you aware of a finite set or the spectrum of quality within it, but lack control over the order for which possible candidates are presented for selection?
Re: Algorithms to Live By – The Computer Science of Human Decisions
#29Earlier quoted context omitted.
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 b…
What is your heuristics based on? Quite often though, you know a little about some thing. How do you adjust your heuristics then? What about the job that I started two months ago, should I expect to work there by December 2023? If the US was founded in 1776, how long will it still exist?
The heuristic is that the average of an interval is the middle. If you know nothing about it other than you're at some point on the time interval, assuming you're at the middle time is a good prior.
When you know more, you certainly should adjust. For the job example, you might think "how long have I usually stayed jobs that have lasted least two months?", "how long do people usually stay in jobs if they make it through the first two months?". Generally speaking, Bayes' theorem is the technical answer to "how do you adjust". Not that I ever actually do that...but I think it's the technically correct answer.
Re: Algorithms to Live By – The Computer Science of Human Decisions
#30Optimal Stopping Problem always confused me, since it seems to assume you’re not aware of a meaningful measure of what an optimal match would be, but aware of what the optimal set of potential matches is. For example, say there’s a goose looking for a mate and they only look at geese of the opposite sex, but in fact, that specific goose’s optimal mate type is a black swan. Maybe it’s just me, but at the point you’re…
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.