Are there any practical applications of UDT that don’t depend on uncertainty as to whether or not I am a simulation, nor on stipulating that one of the participants in a scenario is capable of predicting my decisions with perfect accuracy?
Amnesia: So does UDT roughly-speking direct you to weigh your decisions based on your guesstimate of what decision-relevant facts apply in that scenario? And then choose among available options randomly but weighted by how likely each option is to be optimal in whatever scenario you have actually found yourself in?
Identical copies, (non-identical but very similar players?), players with aligned interests,: I guess this is a special case of dealing with a predictor agent where our predictions of each other’s decisions are likely enough to be accurate that they should be taken into account? So UDT might direct you to disregard causality because you’re confident that the other party will do so the same on their own initiative?
But I don’t understand what this has in common with amnesia scenarios. Is it about disregarding causality?
Non-perfect predictors: Most predictors of anything as complicated as behaviour are VERY imperfect both at the model level and the data-collection level. So wouldn’t the optimal thing to do be to down weigh your confidence in what the other player will do when deciding your own course of action? Unless you have information about how they model you in which case you could try to predict your own behaviour from their perspective?
Are there any practical applications of UDT that don’t depend on uncertainty as to whether or not I am a simulation, nor on stipulating that one of the participants in a scenario is capable of predicting my decisions with perfect accuracy?
Simulations; predictors (not necessarily perfect); amnesia; identical copies; players with aligned interests.
Thank you for answering.
I’m excluding simulations by construction.
Amnesia: So does UDT roughly-speking direct you to weigh your decisions based on your guesstimate of what decision-relevant facts apply in that scenario? And then choose among available options randomly but weighted by how likely each option is to be optimal in whatever scenario you have actually found yourself in?
Identical copies, (non-identical but very similar players?), players with aligned interests,: I guess this is a special case of dealing with a predictor agent where our predictions of each other’s decisions are likely enough to be accurate that they should be taken into account? So UDT might direct you to disregard causality because you’re confident that the other party will do so the same on their own initiative?
But I don’t understand what this has in common with amnesia scenarios. Is it about disregarding causality?
Non-perfect predictors: Most predictors of anything as complicated as behaviour are VERY imperfect both at the model level and the data-collection level. So wouldn’t the optimal thing to do be to down weigh your confidence in what the other player will do when deciding your own course of action? Unless you have information about how they model you in which case you could try to predict your own behaviour from their perspective?