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Not really. The problem as described has two issues that makes it unlike most real problems: 1. The problem only deals with optimising finding the best candida

Not really. The problem as described has two issues that makes it unlike most real problems: 1. The problem only deals with optimising finding the best candidate, the "optimal" solution therefore has a high probability of picking a really bad candidate. In reality this failure mode is quite unacceptable. 2. While many real problems will have some opportunity loss by waiting, it is quite extraordinary that options are presented in such a linear fashion. In a real hiring situation you'd generally be able to go back to a candidate you interviewed earlier and hire them.

bo1024 4y ago
It's even worse than (1), the optimal solution has a high probability of picking no candidate at all.
- wikfwikf 4y ago
  Not picking any candidate at all doesn't matter if you are just trying to optimize your probability of picking the best one. Choosing one which you know not to be the best is not considered any better than choosing none.
- NohatCoder 4y ago
  Some versions of the problem would have you just pick the last candidate then.
TrackerFF 4y ago
Regarding 2: This may seem somewhat contrived, but I tried to come up with some tech analogy. Let's say that you have some analyzer which takes in a finite amount of physical matter, and then produces some dataset. The analysis is a destructive process, so that the analyzer will only be able to produce N different datasets - until it runs out of the physical matter it is analyzing. The datasets are huge, and will take up all available memory. Before the next dataset is finished, you need to decide whether to store or delete/discard the current. The permanent storage can only hold one of these datasets, and it is impossible to write over it. Once you have stored a dataset permanently, the next ones will get discarded as soon as they've been generated. Or something like that. Edit: Wouldn't surprise me if there's some embedded system / machine out there that's bound to select stuff like that.
dataflow 4y ago
> 1. The problem only deals with optimising finding the best candidate, the "optimal" solution therefore has a high probability of picking a really bad candidate. What's "really bad" here and what is the probability of picking a really bad candidate?
- NohatCoder 4y ago
  For a large number of candidates it ends up being approximately 1/e (37%) chance of picking the best candidate, 1-2/e (26%) chance of picking another top candidate, and 1/e (37%) chance of picking a candidate of random skill. That last case occurs when the best candidate was among the first 37% of candidates, which are always rejected, then you run through the rest without finding a better one, and thus end up with the last one interviewed. If you somehow still have to immediately hire or reject all candidates and care about expected average, instead of just finding the best candidate. The optimal solution will have you reject fewer candidates outright, and then have the criteria for how good a candidate should rank to get hired lower gradually throughout interviewing, and drop sharply towards the end of the candidate list.

bo1024 4y ago

It's even worse than (1), the optimal solution has a high probability of picking no candidate at all.

wikfwikf 4y ago
Not picking any candidate at all doesn't matter if you are just trying to optimize your probability of picking the best one. Choosing one which you know not to be the best is not considered any better than choosing none.
NohatCoder 4y ago
Some versions of the problem would have you just pick the last candidate then.

TrackerFF 4y ago

Regarding 2: This may seem somewhat contrived, but I tried to come up with some tech analogy. Let's say that you have some analyzer which takes in a finite amount of physical matter, and then produces some dataset. The analysis is a destructive process, so that the analyzer will only be able to produce N different datasets - until it runs out of the physical matter it is analyzing. The datasets are huge, and will take up all available memory. Before the next dataset is finished, you need to decide whether to store or delete/discard the current. The permanent storage can only hold one of these datasets, and it is impossible to write over it. Once you have stored a dataset permanently, the next ones will get discarded as soon as they've been generated. Or something like that. Edit: Wouldn't surprise me if there's some embedded system / machine out there that's bound to select stuff like that.

dataflow 4y ago

> 1. The problem only deals with optimising finding the best candidate, the "optimal" solution therefore has a high probability of picking a really bad candidate. What's "really bad" here and what is the probability of picking a really bad candidate?

NohatCoder 4y ago
For a large number of candidates it ends up being approximately 1/e (37%) chance of picking the best candidate, 1-2/e (26%) chance of picking another top candidate, and 1/e (37%) chance of picking a candidate of random skill. That last case occurs when the best candidate was among the first 37% of candidates, which are always rejected, then you run through the rest without finding a better one, and thus end up with the last one interviewed. If you somehow still have to immediately hire or reject all candidates and care about expected average, instead of just finding the best candidate. The optimal solution will have you reject fewer candidates outright, and then have the criteria for how good a candidate should rank to get hired lower gradually throughout interviewing, and drop sharply towards the end of the candidate list.