Galleys is a Medium publication of long-form essays on topics like brainstorming, the MVP, optimal stopping, and Into Thin Air.
HN commenters cite its optimal stopping apartment-hunting piece, compared with the 37% rule, and Krakauer's Into Thin Air postscript, which they say adds nuance.
Even with tools to automate the search through listings and get you ahead of other buyers, one of the tricky parts of apartment hunting is its “optimal stopping” structure: you have to decide when to commit without knowing whether an even better apartment might be out there.
This is a famous math problem (the “secretary problem”) with the lovely solution that you should define a time interval over which to search and not commit to anything until you’ve spent 1/e of that time -- 37% -- noncommittally exploring your options.
https://medium.com/galleys/optimal-stopping-45c54da6d8d0#.ljw4cby46
How does this contend with other optimal stopping problems? I just started reading 'Algorithms to Live By' and the very first chapter talks about the 37% rule [1], which feels similar. According to The Dating Problem [2] I should reject the first 37 candidates (when N=100 like in OP's example)
So do I reject the first 9 or 37 candidates?
source:
[1] https://medium.com/galleys/optimal-stopping-45c54da6d8d0
[2] https://medium.com/intriguing-algorithms/thirty-seven-percent-rule-b13c41d83588
Jon Krakauer has responded to Anatoli Boukreev here[0], it seems as like most things in life, there is more nuance this than just hero villain dichotomy.
[0] https://medium.com/galleys/a-postscript-to-into-thin-air-e238d464a256
This other article says around Baltic Avenue was an African-American neighbourhood, not a literal ethnic Baltic neighbourhood. https://medium.com/galleys/behind-the-cardboard-streets-6f1d12c2b756