If all available time is spent optimizing clearly that would be suboptimal since there would be no time left to actually engage in any particular process pursuant to what we value. So the optimal level of optimization is always suboptimal.
I find this confusing because I think it’s (unintentionally) equivocating on what optimizing means.
Naively my response is that if you find you spent too much time trying to optimize for something, you did a suboptimal amount of optimizing. But this wouldn’t mean the “optimal level of optimization is suboptimal”, it would mean “optimizing only for goal B is suboptimal relative to goal A”.
Programmer often talk about this problem as premature optimization: writing code that is optimal for some local concern but comes at the cost of effecient allocation of effort towards the larger goal.
I think perhaps a better way to put what I interepret to be your point would be “the globally optimal amount of local optimization is locally suboptimal”.
I find this confusing because I think it’s (unintentionally) equivocating on what optimizing means.
Naively my response is that if you find you spent too much time trying to optimize for something, you did a suboptimal amount of optimizing. But this wouldn’t mean the “optimal level of optimization is suboptimal”, it would mean “optimizing only for goal B is suboptimal relative to goal A”.
Programmer often talk about this problem as premature optimization: writing code that is optimal for some local concern but comes at the cost of effecient allocation of effort towards the larger goal.
I think perhaps a better way to put what I interepret to be your point would be “the globally optimal amount of local optimization is locally suboptimal”.