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this post was submitted on 02 Sep 2023
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Literally just had this situation.
I still looked for jobs, but since I had a good 3 months of buffer I wasn't hard pressed to take any shotty offers, and was able to accurately apply the algorithm for the Suitors Problem.
Google the Suitors Problem solution for the efficient way to search for jobs and minax your odds of getting a good offer.
The suitors problem (aka the 37% rule, an optimal stopping algorithm) doesn't apply to job offers unless you have to either accept or decline the offers on the spot. A better solution would just be set a deadline of date x, tell each job offer you'll have an offer to them by date x, look at all of your options together, and pick the best.
The time to implement the suitors problem or 37% rule is when you have to accept or decline each option as you see it. You know what you've declined, but not what is left. A very micro example is you have 3 playing cards, and you want as high of a value as possible. (I'm also adding in the threshold rule, it significantly improves the 37% rule).
Card 1: if it's not an ace (98th + percentile), don't keep it
Card 2: if it's higher than card 1, or is a king (90th percentile), keep it. Otherwise, leave it.
Card 3: you have to take it if you didn't take card 1 or card 2.
The purpose of the 37% rule isn't necessarily to pick the best option, just to pick a good option, and it's the best algorithm for the specific scenarios that you apply it in, and is significantly (and statistically) more likely to give you a better option than other methods. It can be applied where choices > 2, has a set max number of choices, and all of the choices are randomly ordered.
I would go into it more, but my last long explanation and examples didn't post correctly. For some great reading (even if you don't have a math background!) I highly recommend Algorithms to Live By by Brian Christen and Tom Griffiths.
Fun fact: the 37% comes from the value of 1/e
Yeah the thing is typically you only have fairly short windows to accept/pass on job offers. At least here in North America, I usually wasn't given a very long opportunity to make my choice for a given offer. Usually tops 1-2 days to say yes or no once an offer is tabled. If I lingered on it too long, they would usually go with someone else who can decide faster.
Which meant usually tops I only had 2-3 offers tops on my table at the exact same time.
By comparison usually the interview process took about a week on average, total.
And, yes, once you decline a tabled offer you are much less likely to be able to go back and get it again, from my experience.
The suitors problem, in a high flow environment where job offers come and go every day on the fly... does indeed apply quite well.
And thats what I did, I estimated I could go for about 4 months if I tightened the pursestrings, so I spent the first month and a bit gathering job offers and simply just writing everything down in a spreadsheet, and created a scoring algorithm to rank them all based on wage, benefits, bonuses, remote vs in office vs hybrid, and a bunch of other variables.
Once I felt satisfied that I had a ranking formula that "felt" right, I took the highest scoring job on that list from the first month of data, and then started actually seriously considering offers and took the first job that gave me an offer that scored higher than that highest score from prior data.
I also recommend Algorithms to Live By, haha, it's an awesome book and I'm gonna +1 that recommendation to anyone else who reads this thread XD
I want to note that by following this process, I over doubled my wage compared to my last job, and if I had just jumped on the first half decent offer I got early on, I would have been making about 2/3rds the wage of the job I actually ended up holding out for.
Ahh I didn't realize some jobs were like that. When I was in my job search last year as a new grad I had 4 job offers and they were all fine with me taking until the end of the month (2 1/2 weeks for the earliest offer, coincidentally the one I picked and am very happy at)