[abeppu]

I think there is an optimization question buried here. In tech lots of people have experience with A/B tests, which function on the assumption that you have a stream of fresh sessions which are independent. Multi-armed bandits, Thompson sampling etc give us frameworks for generalizing this towards finding the best option among a finite set of candidates, if goodness is fixed over time. This is kinda the opposite end of a spectrum: you get to run one policy at a time and the whole premise is that goodness is heavily state dependent. How do you decide whether to keep going with your current policy vs when to start over with another?

Sure, the soup is good ... but is it the best they could have after 52 years? By committing to maintaining one pot for so long, they pay the opportunity cost of not being able to explore related long-lived methods. If there's a different recipe that surpasses this one after only one year of simmering, they'll never find it.

At first I thought this might be related to the secretary problem, but of course if after 50 years of recipe B, you have the option of switching back to recipe A if it's better.

[nonethewiser]

I see no reason to believe the quality could be improved over a more normal cook period. Even super long boils like tonkotsu can be achieved by other means (pressure). IMO its more of a gimmick and frankly kinda gross.

[abeppu]

I mean, I don't want to eat it either. But the point of generalizing the optimization framework is that it applies to other situations with the same shape.

Maybe related questions:

- if you have the resources to run just one online learning model, but can't stash/restore state to just continue a prior model where you left off, how do you find the best online-learned model?

- you have space in your garden for only one large fruit tree. You have an heirloom apple tree currently and it produces decent but not amazing fruit. How do you choose whether to replace with something else, given that a replacement may take years to develop the root system needed to be really productive?

- (more loosely) you're in career track X and have advanced over years, the work is ok but could be better. How should you decide whether to retrain as something different?