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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

#51

Post by robrenaud »

taeric wrote: post #48
maxminminmax wrote: post #46

Earlier quoted context omitted.

>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…

I'm confused, isn't this literally one of the founding problems to "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.

People discuss it as though it has basically any real life applicability, but the assumptions are violated by basically every important real life decision ever.

Don't get me wrong, I love algorithms, CS and math and very much liked learning the secretary problem and solution. I just wouldn't think of it as practically useful.

Re: Algorithms to Live By – The Computer Science of Human Decisions

#52

Post by taeric »

robrenaud wrote: post #51
taeric wrote: post #48

Earlier quoted context omitted.

I'm confused, isn't this literally one of the founding problems to "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.

People discuss it as though it has basically any real life applicability, but the assumptions are violated by basically every important real life decision ever. Don't get me wrong, I love algorithms, CS and math and very much liked learning the secretary problem and solution. I just wouldn't think of it as practically useful.

I'm guessing you see/hear this discussed way more than I do, then. :D

Re: Algorithms to Live By – The Computer Science of Human Decisions

#53

Post by cjohnson318 »

O__________O wrote: post #28

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…

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.

Re: Algorithms to Live By – The Computer Science of Human Decisions

#56

Post by mhuffman »

belter wrote: post #55
gcanyon wrote: post #22

For anyone who's curious, the 37% for optimal stopping is the rounding of 1/e. https://en.wikipedia.org/wiki/Secretary_problem

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

Well, ideally, you want to read 37% of the comments then stop once you read a comment that is better than all the ones you have read so far!

Re: Algorithms to Live By – The Computer Science of Human Decisions

#57

Post by quinncom »

gcanyon wrote: post #22

For anyone who's curious, the 37% for optimal stopping is the rounding of 1/e. https://en.wikipedia.org/wiki/Secretary_problem

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.

Re: Algorithms to Live By – The Computer Science of Human Decisions

#58

Post by isitmadeofglass »

quinncom wrote: post #57
gcanyon wrote: post #22

For anyone who's curious, the 37% for optimal stopping is the rounding of 1/e. https://en.wikipedia.org/wiki/Secretary_problem

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.

[dead]

Re: Algorithms to Live By – The Computer Science of Human Decisions

#59

Post by remram »

fierro wrote: post #37
oski wrote: post #2

I’m curious to know if anyone has “implemented” any of these approaches in their own life…

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

I understand this is a joke but that's not how the algorithm would work.

Re: Algorithms to Live By – The Computer Science of Human Decisions

#60

Post by fierro »

remram wrote: post #59
fierro wrote: post #37

Earlier quoted context omitted.

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

I understand this is a joke but that's not how the algorithm would work.

shoot you're right. Now I need a divorce :(
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