Though, and although I’m not sure I fully understand the formula, I think it’s quite unlikely that it would give rise to a superlinear U. And on reflection, increasing the reward in a superlinear way seems like it could have some advantages but would mostly be outweighed by the system learning to delay finding a solution.
Though we should also note that there isn’t a linear relationship between delay and resources. Increasing returns to scale are common in industrial systems, as scale increases by one unit, the amount that can be done in a given unit of time increases by more than one unit, so a linear utility increase for problems that take longer to solve, may translate to a superlinear utility for increased resources.
Just came across a datapoint, from a talk about generalizing industrial optimization processes, a note about increasing reward over time to compensate for low-hanging fruit exhaustion.
This is the kind of thing I was expecting to see.
Though, and although I’m not sure I fully understand the formula, I think it’s quite unlikely that it would give rise to a superlinear U. And on reflection, increasing the reward in a superlinear way seems like it could have some advantages but would mostly be outweighed by the system learning to delay finding a solution.
Though we should also note that there isn’t a linear relationship between delay and resources. Increasing returns to scale are common in industrial systems, as scale increases by one unit, the amount that can be done in a given unit of time increases by more than one unit, so a linear utility increase for problems that take longer to solve, may translate to a superlinear utility for increased resources.
So I’m not sure what to make of this.