You’ve explained the system. But what’s the motivation behind this?
Even though I only have a high level understanding of what you’re doing, I generally disagree with this kind of approach on a philosophical level. It seems like you’re reifying logical counterfactuals, when I see them more like an analogy, ie. positing a logical counterfactual is an operation that takes place on the level of the map, not the territory.
The general philosophy is deconfusion. Logical counterfactuals show up in several relevant looking places, like functional decision theory. It seems that a formal model of logical counterfactuals would let more properties of these algorithms be proved. There is an important step in going from an intuitive fealing of uncertainty, into a formalized theory of probability. It might also suggest other techniques based on it. I am not sure what you mean by logical counterfactuals being part of the map? Are you saying that they are something an algorithm might use to understand the world, not features of the world itself, like probabilities?
Using this, I think that self understanding, two boxing embedded FDT agents can be fully formally understood, in a universe that contains the right type of hyper-computation.
I mean that there isn’t a property of logical counterfactuals in the universe itself. However, once we’ve created a model (/map) of the universe, we can then define logical counterfactuals as about asking a particular question about this model. We just need to figure out what that question is.
You’ve explained the system. But what’s the motivation behind this?
Even though I only have a high level understanding of what you’re doing, I generally disagree with this kind of approach on a philosophical level. It seems like you’re reifying logical counterfactuals, when I see them more like an analogy, ie. positing a logical counterfactual is an operation that takes place on the level of the map, not the territory.
The general philosophy is deconfusion. Logical counterfactuals show up in several relevant looking places, like functional decision theory. It seems that a formal model of logical counterfactuals would let more properties of these algorithms be proved. There is an important step in going from an intuitive fealing of uncertainty, into a formalized theory of probability. It might also suggest other techniques based on it. I am not sure what you mean by logical counterfactuals being part of the map? Are you saying that they are something an algorithm might use to understand the world, not features of the world itself, like probabilities?
Using this, I think that self understanding, two boxing embedded FDT agents can be fully formally understood, in a universe that contains the right type of hyper-computation.
I mean that there isn’t a property of logical counterfactuals in the universe itself. However, once we’ve created a model (/map) of the universe, we can then define logical counterfactuals as about asking a particular question about this model. We just need to figure out what that question is.