I think utility functions can produce more behaviours than you give them credit for.
Humans don’t have a utility function and make very incoherent decisions. Humans are also the most intelligent organisms on the planet. In fact, it seems to me that the less intelligent an organism is, the easier its behavior can be approximated with model that has a utility function!
The less intelligent organisms are certainly more predictable. But I think that the less intelligent ones actually can’t be described by utility functions and are instead predictable for other reasons. A classic example is the Sphex wasp.
Some Sphex wasps drop a paralyzed insect near the opening of the nest. Before taking provisions into the nest, the Sphex first inspects the nest, leaving the prey outside. During the inspection, an experimenter can move the prey a few inches away from the opening. When the Sphex emerges from the nest ready to drag in the prey, it finds the prey missing. The Sphex quickly locates the moved prey, but now its behavioral “program” has been reset. After dragging the prey back to the opening of the nest, once again the Sphex is compelled to inspect the nest, so the prey is again dropped and left outside during another stereotypical inspection of the nest. This iteration can be repeated several times without the Sphex changing its sequence; by some accounts, endlessly.
So it looks like the wasp has a utility function “ensure the survival of its children” but in fact it’s just following one of a number of fixed “programs”. Whereas humans are actually capable of considering several plans and choosing the one they prefer, which I think is much closer to having a utility function. Of course humans are less predictable, but one would always expect intelligent organisms to be unpredictable. To predict an agent’s actions you essentially have to mimic its thought processes, which will be longer for more intelligent organisms whether they use a utility function or not.
The randomness of human decisions seems essential to human success (on top of other essentials such as speech and cooking). Humans seem to have a knack for sacrificing precious lifetime for fool’s errands that very occasionally create benefit for the entire species.
If trying actions at random produces useful results then a utility maximising AI will choose this course. Utility maximisers consider all plans and pick the one with the highest expected utility, and this can turn out to be one that doesn’t look like it goes directly towards the goal. Eventually of course the AI will have to turn its attention towards its main goal. The question of when to do this is known as the exploration vs. exploitation tradeoff and there are mathematical results that utility maximisers tend to begin by exploring their options and then turn to exploiting their discoveries once they’ve learnt enough.
To define a utility function is to define a (direction towards a) goal. So a discussion of an AI with one, single, unchanging utility function is a discussion of an AI with one, single, unchanging goal. That isn’t just unlike the intelligent organisms we know, it isn’t even a failure mode of intelligent organisms we know. The nearest approximations we have are the least intelligent members of our species.
Again I think that this sort of behaviour (acting towards multiple goals) can be exhibited by utility maximizers. I’ll give a simple example. Consider the agent who can by any 10 fruits from a market, and suppose its utility function is sqrt(number of oranges) + sqrt(number of apples). Then it buys 5 oranges and 5 apples (rather than just buying 10 apples or 10 oranges). The important thing about the example is the the derivative of the utility function is decreasing as the number of oranges increases, and so the more it has already the more it will prefer to buy apples instead. This creates a balance. This is just a simple example but by analogy it would be totally possible to create a utility function to describe a multitude of complex values all simultaneously.
Two agents with identical utility functions are arguably functionally identical to a single agent that exists in two instances. Two agents with utility functions that are not identical are at best irrelevant to each other and at worst implacable enemies.
Just like humans, two agents with different utility functions can cooperate through trade. The two agents calculate the outcome if they trade and the outcome if they don’t trade, and they make the trade if the utility afterwards is higher for both of them. It’s only if their utilities are diametrically opposed that they can’t cooperate.
Agreed on that last point particularly. Especially since, if they want similar enough things, they could easily cooperate without trade.
Like if two AIs supported Alice in her role as Queen of Examplestan, they would probably figure that quibbling with each other over whether Bob the gardener should have one or two buttons undone (just on the basis of fashion, not due to larger consequences) is not a good use of their time.
Also, the utility functions can differ as much as you want on matters aren’t going to come up. Like, Agents A and B disagree on how awful many bad things are. Both agree that they are all really quite bad and all effort should be put forth to prevent them.
I think utility functions can produce more behaviours than you give them credit for.
The less intelligent organisms are certainly more predictable. But I think that the less intelligent ones actually can’t be described by utility functions and are instead predictable for other reasons. A classic example is the Sphex wasp.
So it looks like the wasp has a utility function “ensure the survival of its children” but in fact it’s just following one of a number of fixed “programs”. Whereas humans are actually capable of considering several plans and choosing the one they prefer, which I think is much closer to having a utility function. Of course humans are less predictable, but one would always expect intelligent organisms to be unpredictable. To predict an agent’s actions you essentially have to mimic its thought processes, which will be longer for more intelligent organisms whether they use a utility function or not.
If trying actions at random produces useful results then a utility maximising AI will choose this course. Utility maximisers consider all plans and pick the one with the highest expected utility, and this can turn out to be one that doesn’t look like it goes directly towards the goal. Eventually of course the AI will have to turn its attention towards its main goal. The question of when to do this is known as the exploration vs. exploitation tradeoff and there are mathematical results that utility maximisers tend to begin by exploring their options and then turn to exploiting their discoveries once they’ve learnt enough.
Again I think that this sort of behaviour (acting towards multiple goals) can be exhibited by utility maximizers. I’ll give a simple example. Consider the agent who can by any 10 fruits from a market, and suppose its utility function is sqrt(number of oranges) + sqrt(number of apples). Then it buys 5 oranges and 5 apples (rather than just buying 10 apples or 10 oranges). The important thing about the example is the the derivative of the utility function is decreasing as the number of oranges increases, and so the more it has already the more it will prefer to buy apples instead. This creates a balance. This is just a simple example but by analogy it would be totally possible to create a utility function to describe a multitude of complex values all simultaneously.
Just like humans, two agents with different utility functions can cooperate through trade. The two agents calculate the outcome if they trade and the outcome if they don’t trade, and they make the trade if the utility afterwards is higher for both of them. It’s only if their utilities are diametrically opposed that they can’t cooperate.
Agreed on that last point particularly. Especially since, if they want similar enough things, they could easily cooperate without trade.
Like if two AIs supported Alice in her role as Queen of Examplestan, they would probably figure that quibbling with each other over whether Bob the gardener should have one or two buttons undone (just on the basis of fashion, not due to larger consequences) is not a good use of their time.
Also, the utility functions can differ as much as you want on matters aren’t going to come up. Like, Agents A and B disagree on how awful many bad things are. Both agree that they are all really quite bad and all effort should be put forth to prevent them.