I was trying to understand the point of this, and it looks like it is summed up in
Which algorithm should an agent have to get the best expected value, summing across all possible environments weighted by their probability? The possible environments include those in which threats and promises have been made.
Isn’t it your basic Max EV that is in the core of all decision theories and game theories? The “acausal” part is using the intentional stance for modeling the parts of the universe that are not directly observable, right?
I think it’s a bit complicated? I think CDT would only do the right thing here if it expects it’s future self to be simulated, but would make the wrong choice if it expects it’s past self or present self to be simulated.
I was trying to understand the point of this, and it looks like it is summed up in
Isn’t it your basic Max EV that is in the core of all decision theories and game theories? The “acausal” part is using the intentional stance for modeling the parts of the universe that are not directly observable, right?
I think it’s a bit complicated? I think CDT would only do the right thing here if it expects it’s future self to be simulated, but would make the wrong choice if it expects it’s past self or present self to be simulated.