Thank you for this overview! Unfortunately, I struggle to understand how your proposal differs from a variant of Updateless or Functional decision theory representing decisions as a preset function of observations, chosen so as to yield the best outcome. The trajectories on the possibilities tree still have to be sorted (e.g. by assigning some utility function to each trajectory or to precommitments that the agent makes and keeps or fails along with the outcomes of the branch).
My article is primarily a critique of the independence axiom, not a proposal for a specific alternative decision theory. So I can’t really answer “how does this differ from UDT/FDT” because there is no “this” in this sense, but, as far as I can understand your question, the connection you’re noticing is real and is actually part of the argument.
I argue explicitly (in section on Garrabrant’s comment) that updatelessness and the ergodicity economics critique converge on the same structural insight: both reject branch-by-branch post-update evaluation in favor of holistic policy-level or trajectory-level optimization. So the parallel to UDT/FDT is a feature, not a bug.
On your second point: yes, you still need to rank trajectories or policies. Dropping independence doesn’t mean dropping preferences. What it means is that this ranking need not decompose into the expected value of any function. You can have a complete, transitive preference ordering over trajectories that depends on global properties of the trajectory (like for example time-average growth rate) in ways that no branch-by-branch expected utility can capture. The ranking exists and is well-defined; it’s just not an EU ranking.
I emphasize here ergodicity economics because it provides an example of a principled, non-arbitrary way to construct this trajectory-level objective: derive it from the dynamics of the stochastic process via the ergodic mapping, rather than postulating a utility function and taking its expectation. UDT tells you to optimize over policies but doesn’t specify what to optimize. EE gives you a specific answer grounded in the mathematical structure of the process you’re embedded in.
Thank you for this overview! Unfortunately, I struggle to understand how your proposal differs from a variant of Updateless or Functional decision theory representing decisions as a preset function of observations, chosen so as to yield the best outcome. The trajectories on the possibilities tree still have to be sorted (e.g. by assigning some utility function to each trajectory or to precommitments that the agent makes and keeps or fails along with the outcomes of the branch).
Thanks for your comment!
My article is primarily a critique of the independence axiom, not a proposal for a specific alternative decision theory. So I can’t really answer “how does this differ from UDT/FDT” because there is no “this” in this sense, but, as far as I can understand your question, the connection you’re noticing is real and is actually part of the argument.
I argue explicitly (in section on Garrabrant’s comment) that updatelessness and the ergodicity economics critique converge on the same structural insight: both reject branch-by-branch post-update evaluation in favor of holistic policy-level or trajectory-level optimization. So the parallel to UDT/FDT is a feature, not a bug.
On your second point: yes, you still need to rank trajectories or policies. Dropping independence doesn’t mean dropping preferences. What it means is that this ranking need not decompose into the expected value of any function. You can have a complete, transitive preference ordering over trajectories that depends on global properties of the trajectory (like for example time-average growth rate) in ways that no branch-by-branch expected utility can capture. The ranking exists and is well-defined; it’s just not an EU ranking.
I emphasize here ergodicity economics because it provides an example of a principled, non-arbitrary way to construct this trajectory-level objective: derive it from the dynamics of the stochastic process via the ergodic mapping, rather than postulating a utility function and taking its expectation. UDT tells you to optimize over policies but doesn’t specify what to optimize. EE gives you a specific answer grounded in the mathematical structure of the process you’re embedded in.