ASP doesn’t seem impossible to solve (in the sense of having a decision theory that handles it well and not at the expense of doing poorly on other problems) so why define a class of “fair” problems that excludes it? (I had an idea that I called UDT2 which I think does better on it than UDT1.1 but it’s not as elegant as I hoped.) Defining such problem classes may be useful for talking about the technical properties of specific decision theories, but that doesn’t seem to be what you’re trying to do here. The only other motivation I can think of is finding a way to justify not solving certain problems, but I don’t think that makes sense in the case of ASP.
“ASP doesn’t seem impossible to solve (in the sense of having a decision theory that handles it well and not at the expense of doing poorly on other problems) so why define a class of “fair” problems that excludes it?”—my intuition is the opposite, that doing well on such problems means doing poorly on others.
Can you explain your intuition? (Even supposing your intuition is correct, it still doesn’t seem like defining a “fair” class of problems is that useful. Shouldn’t we instead try to find a decision theory that offers the best trade-offs on the actual distribution of decision problems that we (or our AIs) will be expected to face?)
To explain my intuition, suppose we had a decision theory that does well on ASP-like problems and badly on others, and a second decision theory that does badly on ASP-like problems and well on others, then we can create a meta decision theory that first tries to figure out what kind of problem it is facing and then select one of these decision theories to solve it. This meta decision theory would itself be a decision theory that does well on both types of problems so such a decision theory ought to exist.
BTW, you can quote others by putting a quote in a separate paragraph and putting “>” in front of it.
It still doesn’t seem like defining a “fair” class of problems is that useful”—discovering one class of fair problems lead to CDT. Another lead to TDT. This theoretical work is seperate from the problem of producing pragmatic algorithms that deal with unfairness, but both approaches produce insights.
“This meta decision theory would itself be a decision theory that does well on both types of problems so such a decision theory ought to exist”—I currently have a draft post that does allow some kinds of rewards based on algorithm internals to be considered fair and which basically does the whole meta-decision theory thing (that section of the draft post was written a few hours after I asked this question which is why my views in it are slightly different).
ASP doesn’t seem impossible to solve (in the sense of having a decision theory that handles it well and not at the expense of doing poorly on other problems) so why define a class of “fair” problems that excludes it? (I had an idea that I called UDT2 which I think does better on it than UDT1.1 but it’s not as elegant as I hoped.) Defining such problem classes may be useful for talking about the technical properties of specific decision theories, but that doesn’t seem to be what you’re trying to do here. The only other motivation I can think of is finding a way to justify not solving certain problems, but I don’t think that makes sense in the case of ASP.
“ASP doesn’t seem impossible to solve (in the sense of having a decision theory that handles it well and not at the expense of doing poorly on other problems) so why define a class of “fair” problems that excludes it?”—my intuition is the opposite, that doing well on such problems means doing poorly on others.
Can you explain your intuition? (Even supposing your intuition is correct, it still doesn’t seem like defining a “fair” class of problems is that useful. Shouldn’t we instead try to find a decision theory that offers the best trade-offs on the actual distribution of decision problems that we (or our AIs) will be expected to face?)
To explain my intuition, suppose we had a decision theory that does well on ASP-like problems and badly on others, and a second decision theory that does badly on ASP-like problems and well on others, then we can create a meta decision theory that first tries to figure out what kind of problem it is facing and then select one of these decision theories to solve it. This meta decision theory would itself be a decision theory that does well on both types of problems so such a decision theory ought to exist.
BTW, you can quote others by putting a quote in a separate paragraph and putting “>” in front of it.
It still doesn’t seem like defining a “fair” class of problems is that useful”—discovering one class of fair problems lead to CDT. Another lead to TDT. This theoretical work is seperate from the problem of producing pragmatic algorithms that deal with unfairness, but both approaches produce insights.
“This meta decision theory would itself be a decision theory that does well on both types of problems so such a decision theory ought to exist”—I currently have a draft post that does allow some kinds of rewards based on algorithm internals to be considered fair and which basically does the whole meta-decision theory thing (that section of the draft post was written a few hours after I asked this question which is why my views in it are slightly different).