For what I skimmed from your paper, it looks like the LH agents may be viewed as discrete optimization processes trying to optimize an objective/utility function across an infinite space of possible environments (infinite voters).
If it is the case, and if each environment vote has the same weight, you may be in a case of no free lunch, where the performances of all possible agents (including the random agent) will average to the same across all possible environments.
Or, to restate the above, for each environment in which an agent is doing well, it is possible to construct an "anti-environment" where the agent is performing exactly as bad.
My personal opinion on the topic of AGI is that it is actually a case of NFLT.
For what I skimmed from your paper, it looks like the LH agents may be viewed as discrete optimization processes trying to optimize an objective/utility function across an infinite space of possible environments (infinite voters).
If it is the case, and if each environment vote has the same weight, you may be in a case of no free lunch, where the performances of all possible agents (including the random agent) will average to the same across all possible environments.
Or, to restate the above, for each environment in which an agent is doing well, it is possible to construct an "anti-environment" where the agent is performing exactly as bad.
My personal opinion on the topic of AGI is that it is actually a case of NFLT.