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).
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).