Thanks for your comment! I find your line of reasoning in the ASP problem and the Coin Flip Creation plausible. So your point is that, in both cases, by choosing a decision algorithm, one also gets to choose where this algorithm is being instantiated? I would say that in the CFC, choosing the right action is sufficient, while in the ASP you also have to choose the whole UDP program so as to be instantiated in a beneficial way (similar to the distinction of how TDT iterates over acts and UDT iterates over policies).
Would you agree that the Coin Flip Creation is similar to e.g. the Smoking Lesion? I could also imagine that by not smoking, UDT would become more likely to be instantiated in a world where the UDT agent doesn’t have the gene (or that the gene would eliminate (some of) the UDT agents from the worlds where they have cancer). Otherwise there couldn’t be a study showing a correlation between UDT agents’ genes and their smoking habits. If the participants of the study used a different decision theory or, unlike us, didn’t have knowledge of the results of the study, UDT would probably smoke. But in this case I would argue that EDT would do so as well, since conditioning on all of this information puts it out of the reference class of the people in the study.
One could probably generalize this kind of “likelihood of being instantiated” reasoning. My guess would be that an UDT version that takes it into account might behave according to conditional probabilities like EDT. Take e.g. the example from this post by Nate Soares. If there isn’t a principled difference to the Coin Flip Case that I’ve overlooked, then UDT might reason that if it takes “green”, it will become very likely that it will be instantiated only in a world where gamma rays hit the UDT agent (since apparently, UDT agents that choose green are “eliminated” from worlds without gamma rays – or at least that’s what I have to assume if I don’t know any additional facts). Therefore our specified version of UDT takes the red box. The main argument I’m trying to make is that if you solve the problem like this, then UDT would (at least here, and possibly in all cases) become equivalent to updateless EDT. Which as far as I know would be a relief, since (u)EDT seems easier to formalize?
So your point is that, in both cases, by choosing a decision algorithm, one also gets to choose where this algorithm is being instantiated?
To clarify, it’s the algorithm itself that chooses how it behaves. So I’m not talking about how algorithm’s instantiation depends on the way programmer chooses to write it, instead I’m talking about how algorithm’s instantiation depends on the choices that the algorithm itself makes, where we are talking about a particular algorithm that’s already written. Less mysteriously, the idea of algorithm’s decisions influencing things describes a step in the algorithm, it’s how the algorithm operates, by figuring out something we could call “how algorithm’s decisions influence outcomes”. The algorithm then takes that thing and does further computations that depend on it.
Thanks for your comment! I find your line of reasoning in the ASP problem and the Coin Flip Creation plausible. So your point is that, in both cases, by choosing a decision algorithm, one also gets to choose where this algorithm is being instantiated? I would say that in the CFC, choosing the right action is sufficient, while in the ASP you also have to choose the whole UDP program so as to be instantiated in a beneficial way (similar to the distinction of how TDT iterates over acts and UDT iterates over policies).
Would you agree that the Coin Flip Creation is similar to e.g. the Smoking Lesion? I could also imagine that by not smoking, UDT would become more likely to be instantiated in a world where the UDT agent doesn’t have the gene (or that the gene would eliminate (some of) the UDT agents from the worlds where they have cancer). Otherwise there couldn’t be a study showing a correlation between UDT agents’ genes and their smoking habits. If the participants of the study used a different decision theory or, unlike us, didn’t have knowledge of the results of the study, UDT would probably smoke. But in this case I would argue that EDT would do so as well, since conditioning on all of this information puts it out of the reference class of the people in the study.
One could probably generalize this kind of “likelihood of being instantiated” reasoning. My guess would be that an UDT version that takes it into account might behave according to conditional probabilities like EDT. Take e.g. the example from this post by Nate Soares. If there isn’t a principled difference to the Coin Flip Case that I’ve overlooked, then UDT might reason that if it takes “green”, it will become very likely that it will be instantiated only in a world where gamma rays hit the UDT agent (since apparently, UDT agents that choose green are “eliminated” from worlds without gamma rays – or at least that’s what I have to assume if I don’t know any additional facts). Therefore our specified version of UDT takes the red box. The main argument I’m trying to make is that if you solve the problem like this, then UDT would (at least here, and possibly in all cases) become equivalent to updateless EDT. Which as far as I know would be a relief, since (u)EDT seems easier to formalize?
To clarify, it’s the algorithm itself that chooses how it behaves. So I’m not talking about how algorithm’s instantiation depends on the way programmer chooses to write it, instead I’m talking about how algorithm’s instantiation depends on the choices that the algorithm itself makes, where we are talking about a particular algorithm that’s already written. Less mysteriously, the idea of algorithm’s decisions influencing things describes a step in the algorithm, it’s how the algorithm operates, by figuring out something we could call “how algorithm’s decisions influence outcomes”. The algorithm then takes that thing and does further computations that depend on it.