Maybe the easiest way to understand UDT and TDT is:
UDT = EDT without updating on sensory inputs, with “actions” to be understood as logical facts about the agent’s outputs
TDT = CDT with “causality” to be understood as Pearl’s notion of causality plus additional arrows for logical correlations
Comparing UDT and TDT directly, the main differences seem to be that UDT does not do Bayesian updating on sensory inputs and does not make use of causality. There seems to be general agreement that Bayesian updating on sensory inputs is wrong in a number of situations, but disagreement and/or confusion about whether we need causality. Gary Drescher put it this way:
Plus, if you did have a general math-counterfactual-solving module, why would you relegate it to the logical-dependency-finding subproblem in TDT, and then return to the original factored causal graph? Instead, why not cast the whole problem as a mathematical abstraction, and then directly ask your math-counterfactual-solving module whether, say, (Platonic) C’s one-boxing counterfactually entails (Platonic) $1M? (Then do the argmax over the respective math-counterfactual consequences of C’s candidate outputs.)
(Eliezer didn’t give an answer. ETA: He did answer a related question here.)
I can see what updating on sensory updating does to TDT (causing it to fail counterfactual mugging). But what does it mean to say that TDT makes use of causality and UDT doesn’t? Are there any situations where this causes them to give different answers?
(I added a link at the end of the grandparent comment where Eliezer does give some of his thoughts on this issue.)
Are there any situations where this causes them to give different answers?
Eliezer seems to think that causality can help deal with Gary Drescher’s “5-and-10” problem:
But you would still have to factor out your logical uncertainty in a way which prevented you from concluding “if I choose A6, it must have had higher utility than A7” when considering A6 as an option (as Drescher observes).
But it seems possible to build versions of UDT that are free from such problems (such as the proof-based ones that cousin_it and Nesov have explored), although there are still some remaining issues with “spurious proofs” which may be related. In any case, it’s unclear how to get help from the notion of causality, and as far as I know, nobody has explored in that direction and reported back any results.
Maybe the easiest way to understand UDT and TDT is:
UDT = EDT without updating on sensory inputs, with “actions” to be understood as logical facts about the agent’s outputs
TDT = CDT with “causality” to be understood as Pearl’s notion of causality plus additional arrows for logical correlations
Comparing UDT and TDT directly, the main differences seem to be that UDT does not do Bayesian updating on sensory inputs and does not make use of causality. There seems to be general agreement that Bayesian updating on sensory inputs is wrong in a number of situations, but disagreement and/or confusion about whether we need causality. Gary Drescher put it this way:
(Eliezer didn’t give an answer. ETA: He did answer a related question here.)
I can see what updating on sensory updating does to TDT (causing it to fail counterfactual mugging). But what does it mean to say that TDT makes use of causality and UDT doesn’t? Are there any situations where this causes them to give different answers?
(I added a link at the end of the grandparent comment where Eliezer does give some of his thoughts on this issue.)
Eliezer seems to think that causality can help deal with Gary Drescher’s “5-and-10” problem:
But it seems possible to build versions of UDT that are free from such problems (such as the proof-based ones that cousin_it and Nesov have explored), although there are still some remaining issues with “spurious proofs” which may be related. In any case, it’s unclear how to get help from the notion of causality, and as far as I know, nobody has explored in that direction and reported back any results.