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I’m curious to know if anyone has “implemented” any of these approaches in their own life…
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.
Brian Christian, the author of "Algorithms to Live By", has also written "The Alignment Problem" on the technical and moral questions of A.I.
Interesting article, I would recommend also reading Russ Robert's 'Wild Problems', who makes the case that algorithms are a bad fit for many of life's big decisions. Podcast and transcript on it here. https://www.econtalk.org/russ-roberts-and-mike-munger-on-wil...
Interesting article, I would recommend also reading Russ Robert's 'Wild Problems', who makes the case that algorithms are a bad fit for many of life's big decisions. Podcast and transcript on it here. https://www.econtalk.org/russ-roberts-and-mike-munger-on-wil...
Isn't that just using the subconscious's algorithms instead of the conscious's?
For me the key thing is that when something is too complicated to quantify, attempting to quantify it will result in worse decisions. A bit like Hayek's calculation problem for the economy but for personal decisions.
Optimal 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.…
For what value function? It is basically never the case that my value function is "all choices other than the optimal are equally bad" -- which is what this rule is based on.
As a personal opinion, this drives me up the wall. There is a great problem here, and there is a whole area (several of them, actually!) of applied math dedicated to it (Statistical Decision Theory, Reinforcement Learning, you name it). Instead we get this toy version -- which at best is an oversimplified intro to he subject, and at worst an excuse to bamboozle with math-fairy-dust -- brought out as some kind of rule "to live by". Your algorithm is bad, and you should feel bad.
Interesting article, I would recommend also reading Russ Robert's 'Wild Problems', who makes the case that algorithms are a bad fit for many of life's big decisions. Podcast and transcript on it here. https://www.econtalk.org/russ-roberts-and-mike-munger-on-wil...
Isn't that just using the subconscious's algorithms instead of the conscious's?
It's a useless (tautological) statement unless we start with a good definition of what is and is not an "algorithm". From a cursory glance, this seems trickier than it looks, and once we have a constrained definition it's not clear any more that human minds operate in the same framework (strong claims require strong evidence).
Eg: If we define algorithms as what can be implemented on a Turing machine, then we're necessarily talking deterministic algorithms (allowing pseudorandomness), etc.
Earlier quoted context omitted.
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.…
>but probabilistically the strategy is optimal. For what value function? It is basically never the case that my value function is "all choices other than the optimal are equally bad" -- which is what this rule is based on. As a personal opinion, this drives me up the wall. There is a great problem here, and there is a whole area (several of them, actually!) of applied math dedicated to it (Statistical Decision Theory…
That is, this may be a simplified version of the problem, but it is a legit problem from that field. And the results being presented here don't disagree with the legit problem, do they?
Now, is it a simplification of a simplification? Sure. I'm not clear on why it is as bad as you are putting forth, though.
I’m curious to know if anyone has “implemented” any of these approaches in their own life…
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 stoppi…
Earlier quoted context omitted.
Isn't that just using the subconscious's algorithms instead of the conscious's?
Calling everything an algorithm rests on some implicit/vague assumption of computational universality (that subsumes human functioning!) which seems quite non-obvious. It's a useless (tautological) statement unless we start with a good definition of what is and is not an "algorithm". From a cursory glance, this seems trickier than it looks, and once we have a constrained definition it's not clear any more that human…