> If you don’t know this trade-off, then you don’t have a decision problem Let me clarify what I mean. In the cases I've worked with professionally (finance,

> If you don’t know this trade-off, then you don’t have a decision problem Let me clarify what I mean. In the cases I've worked with professionally (finance, autonomous driving), no-one gives you a reward function. You have a rough idea of the outcome you want, and you fiddle around with reward functions until your algirithm kinda-sorta delivers the outcomes you knew you wanted in the first place. In this sense, the OP gets it precisely backwards. OP wants some sort of hyper-rational Bayesian model that optimises outcomes based on the one true loss function. There is no correct reward function, and objective functions in general tend to be far too simple to capture the nuance one encounters in the real world. > If you did not even know what objective you are pursuing at all, you would not be using an ML model... It's not that you don't know at all, but you should be very clear that your objective is a fudge that probably doesn't capture what you really want. Maths is clean: the real world is messy.