I think I may be misunderstanding your model, but, well, here’s an example of where I think yours (ie, just using the built in error terms) would fail worse than mine:
Imagine that in addition to you, there’re, say, a thousand systems that are somewhat explicitly dependent on algorithm A1 (or try to be) and another thousand that are explicitly dependent on A2 (or try to be), either through directly implementing, or modeling, or...
If you are A1, then your decision will be linked to the first group and less so to the second group… and if you are A2, then the other way around. Just using error terms would weaken all the couplings without noticing that if one is A2, while one is no longer coupled to the first group, they are to the second.
Does that make sense?
And again, I know that error correction and so on can and should be used to ensure lower probability of “algorithm you’re trying to implement not being what you actually are implementing”, but right now I’m just focusing on “how can we represent that sort of situation?”
I may be misunderstanding your solution to the problem, though.
I’m going to wait for at least one person other than you or me to join this discussion before saying anything further, just as a “sanity check” and to see what kind of miscommunication might be going on.
I’ve followed along. But I’ve been hesitant to join on because it seemed to me that this question was being raised to a meta-level that it didn’t necessarily deserve.
In the grandparent, for example, why can I not model my uncertainty about how the other agents will behave using the same general mechanism I use for everything else I’m uncertain about? It’s not all that special, at least for these couple of examples. (Of course the more general question of failure detection and mitigation, completely independent of any explicitly dependant mind reading demigods or clones is another matter but doesn’t seem to be what the conversation is about...)
As for a sanity check, such as I can offer: The grandparent seems correct in stating that Silas’s graph doesn’t handle the problem described in the grandparent. Just because it is a slightly different problem. With the grandparent’s problem it seems to be the agent’s knowledge of likely hardware failure modes that is important rather than Omega’s.
As for a sanity check, such as I can offer: The grandparent seems correct in stating that Silas’s graph doesn’t handle the problem described in the grandparent. Just because it is a slightly different problem. With the grandparent’s problem it seems to be the agent’s knowledge of likely hardware failure modes that is important rather than Omega’s
Well, Psy-Kosh had been repeatedly bringing up that Omega has to account for how something might happen between me choosing an algorithm, and the algorithm I actually implement, because of cosmic rays and whatnot, so I thought that one was more important.
However, I think the “innards” node already contains one’s knowledge about what kinds of things could go wrong. If I’m wrong, add that as a parent to the boxed node. the link is clipped when you compute the “would” anyway.
OOOOOOH! I think I see (part of, but not all) of the misunderstanding here. I wasn’t talking about how Omega can take this into account, I was talking about how the agent Omega is playing games with would take this into account.
ie, not how Omega deals with the problem, but how I would.
Problems involving Omega probably aren’t useful examples for demonstrating your problem either way since Omega will accurately predict our actions either way and our identity angst is irrelevant.
I’d like to see an instantiation of the type of problem you mentioned above, involving the many explicitly dependant systems. Something involving a box to pick or a bet to take. Right now the requirements of the model are not defined much beyond ‘apply standard decision theory with included mechanism for handling uncertainty at such time as the problem becomes available’.
I think I may be misunderstanding your model, but, well, here’s an example of where I think yours (ie, just using the built in error terms) would fail worse than mine:
Imagine that in addition to you, there’re, say, a thousand systems that are somewhat explicitly dependent on algorithm A1 (or try to be) and another thousand that are explicitly dependent on A2 (or try to be), either through directly implementing, or modeling, or...
If you are A1, then your decision will be linked to the first group and less so to the second group… and if you are A2, then the other way around. Just using error terms would weaken all the couplings without noticing that if one is A2, while one is no longer coupled to the first group, they are to the second.
Does that make sense?
And again, I know that error correction and so on can and should be used to ensure lower probability of “algorithm you’re trying to implement not being what you actually are implementing”, but right now I’m just focusing on “how can we represent that sort of situation?”
I may be misunderstanding your solution to the problem, though.
I’m going to wait for at least one person other than you or me to join this discussion before saying anything further, just as a “sanity check” and to see what kind of miscommunication might be going on.
Fair enough
I’ve followed along. But I’ve been hesitant to join on because it seemed to me that this question was being raised to a meta-level that it didn’t necessarily deserve.
In the grandparent, for example, why can I not model my uncertainty about how the other agents will behave using the same general mechanism I use for everything else I’m uncertain about? It’s not all that special, at least for these couple of examples. (Of course the more general question of failure detection and mitigation, completely independent of any explicitly dependant mind reading demigods or clones is another matter but doesn’t seem to be what the conversation is about...)
As for a sanity check, such as I can offer: The grandparent seems correct in stating that Silas’s graph doesn’t handle the problem described in the grandparent. Just because it is a slightly different problem. With the grandparent’s problem it seems to be the agent’s knowledge of likely hardware failure modes that is important rather than Omega’s.
Well, Psy-Kosh had been repeatedly bringing up that Omega has to account for how something might happen between me choosing an algorithm, and the algorithm I actually implement, because of cosmic rays and whatnot, so I thought that one was more important.
However, I think the “innards” node already contains one’s knowledge about what kinds of things could go wrong. If I’m wrong, add that as a parent to the boxed node. the link is clipped when you compute the “would” anyway.
OOOOOOH! I think I see (part of, but not all) of the misunderstanding here. I wasn’t talking about how Omega can take this into account, I was talking about how the agent Omega is playing games with would take this into account.
ie, not how Omega deals with the problem, but how I would.
Problems involving Omega probably aren’t useful examples for demonstrating your problem either way since Omega will accurately predict our actions either way and our identity angst is irrelevant.
I’d like to see an instantiation of the type of problem you mentioned above, involving the many explicitly dependant systems. Something involving a box to pick or a bet to take. Right now the requirements of the model are not defined much beyond ‘apply standard decision theory with included mechanism for handling uncertainty at such time as the problem becomes available’.
So? The graph still handles that.