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Notes -
Thanks for this!
One modification I wonder if you'd be willing to simulate for us: what if we don't strictly require stopping at n? You could formalize it to something like "given a strategy that on average samples n suitors when run it to its stopping point, what are the loss/etc?" p(miss) becomes 0 by definition.
That feels more like real life: if the goal is to stop dating by 35, but I happen to be dating Hitler when I hit 35, I'm probably gonna push it to 36.
It seems to me like the best way to model this would be to have some multiplicative scaling factor on utility that diminishes over time, since 50 years of your life with the second best suitor is (probably) going to be better than spending 20 years of your life with the best suitor. Perhaps a linear decay to simulate amount of lifespan remaining, so the utility of choosing the nth suitor is their actual quality multiplied by (1-0.01n). Or maybe weight it more towards the beginning to account for youth and childbearing years, like (0.99)^n or something.
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It's interesting what you suggested is almost the opposite of the scenario @Felagund suggested. I suppose a hopeless romantic would not want to risk the potentially corrosive effect of having knowingly settled. I assume that in practice you would combine some knowledge of the current rate, the steepness of the expected falloff, some pure romantic inclination, and some fear of missing out into some heuristic.
In the scenario where we keep n from above, but keep going if we still haven't found the one I do think is interesting. If we set our benchmark at r=sqrt(n), 83% of the time you find your partner before n/e. Assuming (offset) exponentially distributed utility, the expected utility in this case is about the same as in the case where we assumed halting. I guess this is like the plethora of people who marry someone they meet in college? In about 10% of the cases there you manage to find a partner before the expected window closes, and patients is rewarded with about 50% more utility (4.5 vs 3).
I then assumed some very questionable things to set the next boundaries. First, we can transpose to time as above. Second, that we care about marriage with respect to producing children. Putting geriatric maternal age at 35-40, and assuming you would just offset paternal age so we don't have to deal with an extra set of scenarios, I find a new cutoff of 320/256. I think this sort of accommodates @jeroboam's point. In that case not stopping, but being willing to continue into the danger zone, 1.3% of the runs find the one by "40." Of course expected utility is higher at 5.2, but being willing to push age, but unwilling to settle only picked up a small number of additional "successes."
In the remaining 5% cases you eventually find your soulmate with an expected utility of 6.4. You do have to wait exponentially long though, with a median age equivalent of 67, and a mean of 343!
Setting the high water mark at n/e, but being unwilling to stop is similar in utility. Now you've eliminated the 3 unit of expected utility bucket, and the 4.5 unit utility bucket has 63% weight. Your willingness to go into the (questionably) age equivalent 35-40 bucket also preserves 7% of the trials. By setting your benchmark so late though, 30% of the time you miss the critical window. The higher expected utility, I guess, represents it being totally worth it to find your soul mate, assuming there was no penalty for waiting past geriatric pregnancy age.
@self_made_human don't worry I know these simulations are entirely irrelevant to us denizen of themotte, thus the fun thread and why I included the note on n <= 7, ಥ_ಥ
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I really hope I don't have to resort to necrophilia by the time I'm 36, but either way, I'm sure the coroner will cut me some slack.
We all share that same hope, if I might dare a post that suggests consensus.
Mods, twist his nuts
(I really had to use all my willpower not to put the mod hat on for this comment, be thankful)
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