I can follow most of this, but i’m confused about one part of the premise.
What if the agent created a low-resolution simulation of its behavior, called it Approximate Self, and used that in its predictions? Is the idea that this is doable, but represents a unacceptably large loss of accuracy? Are we in a ‘no approximation’ context where any loss of accuracy is to be avoided?
My perspective: It seems to me that humans also suffer from the problem of embedded self-reference. I suspect that humans deal with this by thinking about a highly approximate representation of their own behavior. For example, when i try to predict how a future conversation will go, i imagine myself saying things that a ‘reasonable person’ might say. Could a machine use a analogous form of non-self-referential approximation?
In order to do this, the agent needs to be able to reason approximately about the results of their own computations, which is where logical uncertainty comes in
I can follow most of this, but i’m confused about one part of the premise.
What if the agent created a low-resolution simulation of its behavior, called it Approximate Self, and used that in its predictions? Is the idea that this is doable, but represents a unacceptably large loss of accuracy? Are we in a ‘no approximation’ context where any loss of accuracy is to be avoided?
My perspective: It seems to me that humans also suffer from the problem of embedded self-reference. I suspect that humans deal with this by thinking about a highly approximate representation of their own behavior. For example, when i try to predict how a future conversation will go, i imagine myself saying things that a ‘reasonable person’ might say. Could a machine use a analogous form of non-self-referential approximation?
Great piece, thanks for posting.
In order to do this, the agent needs to be able to reason approximately about the results of their own computations, which is where logical uncertainty comes in