It's a simple question, but I think it might help if I add in context. In the paper introducing Functional Decision Theory, it is noted that it is impossible to design an algorithm that can perform well on all decision problems since some of them can be specified to be blatantly unfair, ie. punish every agent that isn't an alphabetical decision theorist.
The question then arises, how do we define which problems are or are not fair? We start by noting that some people consider Newcomb's-like problems to be unfair since your outcome depends on a predictor's prediction, which is rooted in an analysis of your algorithm. So what makes this case any different from only rewarding the alphabetical decision theorist?
The paper answers that the prediction only depends on the decision you end up making and that any other internal details are ignored. So it only cares about your decision and not how you come to it, the problem seems fair. I'm inclined to agree with this reasoning, but a similar line of reasoning doesn't seem to hold with Agent Simulates Predictor. Here the algorithm you use is relevant as the predictor can only predict the agent if it's algorithm is less than a certain level of complexity, otherwise it may make a mistake.
Please note that this question isn't about whether this problem is worth considering; life is often unfair and we have to deal with it the best that we can. The question is about whether the problem is "fair", where I roughly understand "fair" meaning that this is in a certain class of problems that I can't specify at this moment (I suspect it would require its own seperate post) where we should be able to achieve the optimal result in each problem.
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 algor... (read more)