The not so operationalized answer is that a good operationalization is one that are helpful for achieving alignment.
An operationalization of [helpfulness of an operationalization] would give some sorts to gears level understanding of what shape the operationalization should have to be helpful. I don’t have any good model for this, so I will just gesture vaguely.
I think that mathematical descriptions are good, since they are more precise. My first operationalization attempt is pretty mathematical which is good. It is also more “constructive” (not sure if this is the exact right word), i.e. it describes alignment in terms of internal properties, rather than outcomes. Internal properties are more useful as design guidelines, as long as they are correct. The big problem with my first operationalization is that it don’t actually point to what we want.
The problem with the second attempt is that it just states what outcome we want. There is nothing in there to help us achieve it.
Can’t you restate the second one as the relationship between two utility functions UA and UB such that increasing one (holding background conditions constant) is guaranteed not to decrease the other? I.e. their respective derivatives are always non-negative for every background condition.
Yes, I like this one. We don’t want the AI to find a way to give it self utility while making things worse for us. And if we are trying to make things better for us, we don’t want the AI to resist us.
Do you want to find out what these inequalities implies about the utility functions? Can you find examples where your condition is true for non-identical functions?
UA changes only when MA (A’s world model) changes which is ultimately caused by new observations, i.e. changes in the world state (let’s assume that both A and B perceive the world quite accurately).
If whenever UA changes UB doesn’t decrease, then whatever change in the world increased UA, B at least doesn’t care. This is problematic when A and B need the same scarce resources (instrumental convergence etc). It could be satisfied if they were both satisficers or bounded agents inhabiting significantly disjoint niches.
A robust solution seems seems to be to make (super accurately modeled) UB a major input to UA.
Then (I think) for your inequality to hold, it must be that
U_B = f(3x+y), where f’ >= 0
If U_B care about x and y in any other proportion, then B can make trade-offs between x and y which makes things better for B, but worse for A.
This will be true (in theory) even if both A and B are satisfisers. You can see this by assuming replacing y and x with sigmoids of some other variables.
This is a good question.
The not so operationalized answer is that a good operationalization is one that are helpful for achieving alignment.
An operationalization of [helpfulness of an operationalization] would give some sorts to gears level understanding of what shape the operationalization should have to be helpful. I don’t have any good model for this, so I will just gesture vaguely.
I think that mathematical descriptions are good, since they are more precise. My first operationalization attempt is pretty mathematical which is good. It is also more “constructive” (not sure if this is the exact right word), i.e. it describes alignment in terms of internal properties, rather than outcomes. Internal properties are more useful as design guidelines, as long as they are correct. The big problem with my first operationalization is that it don’t actually point to what we want.
The problem with the second attempt is that it just states what outcome we want. There is nothing in there to help us achieve it.
Can’t you restate the second one as the relationship between two utility functions UA and UB such that increasing one (holding background conditions constant) is guaranteed not to decrease the other? I.e. their respective derivatives are always non-negative for every background condition.
∂UA∂UB≥0∧∂UB∂UA≥0
Yes, I like this one. We don’t want the AI to find a way to give it self utility while making things worse for us. And if we are trying to make things better for us, we don’t want the AI to resist us.
Do you want to find out what these inequalities implies about the utility functions? Can you find examples where your condition is true for non-identical functions?
I don’t have a specific example right now but some things that come to mind:
Both utility functions ultimately depend in some way on a subset of background conditions, i.e. the world state
The world state influences the utility functions through latent variables in the agents’ world models, to which they are inputs.
UA changes only when MA (A’s world model) changes which is ultimately caused by new observations, i.e. changes in the world state (let’s assume that both A and B perceive the world quite accurately).
If whenever UA changes UB doesn’t decrease, then whatever change in the world increased UA, B at least doesn’t care. This is problematic when A and B need the same scarce resources (instrumental convergence etc). It could be satisfied if they were both satisficers or bounded agents inhabiting significantly disjoint niches.
A robust solution seems seems to be to make (super accurately modeled) UB a major input to UA.
Lets say that
U_A = 3x + y
Then (I think) for your inequality to hold, it must be that
U_B = f(3x+y), where f’ >= 0
If U_B care about x and y in any other proportion, then B can make trade-offs between x and y which makes things better for B, but worse for A.
This will be true (in theory) even if both A and B are satisfisers. You can see this by assuming replacing y and x with sigmoids of some other variables.