Another point is that when you optimize relentlessly for one thing, you have might have trouble exploring the space adequately (get stuck at local maxima). That’s why RL agents/algorithms often take random actions when they are training (they call this “exploration” instead of “exploitation”). Maybe random actions can be thought of as a form of slack? Micro-slacks?
Look at Kenneth Stanley’s arguments about why objective functions are bad (video talk on it here). Basically he’s saying we need a lot more random exploration. Humans are similar—we have an open-ended drive to explore in addition to drives to optimize a utility function. Of course maybe you can argue the open-ended drive to explore is ultimately in the service of utility optimization, but you can argue the same about slack, too.
Another point is that when you optimize relentlessly for one thing, you have might have trouble exploring the space adequately (get stuck at local maxima). That’s why RL agents/algorithms often take random actions when they are training (they call this “exploration” instead of “exploitation”). Maybe random actions can be thought of as a form of slack? Micro-slacks?
Look at Kenneth Stanley’s arguments about why objective functions are bad (video talk on it here). Basically he’s saying we need a lot more random exploration. Humans are similar—we have an open-ended drive to explore in addition to drives to optimize a utility function. Of course maybe you can argue the open-ended drive to explore is ultimately in the service of utility optimization, but you can argue the same about slack, too.