I don't think so. But I can give you the critique here :)

SUMMARY: If you don't actually calculate expected utility, don't expect to automatically make choices that correspond to a relevant utility function. Also, don't name a specific function for a general feeling - you might accidentally start calling the function when you just mean the feeling, and then you get really wrong answers.

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I've already made the obvious criticism - since Stuart's anthropic decision theory is basically a way to avoid using subjective probabilities (also known as "probabilities") when they're confusing, it makes sense that it usually can't do probability-associated things like maximizing the expectation of a specific utility function.

The replacement for actually figuring out the probabilities is an "anthropic preference" that takes a list of utilities of different people you could be inside a "world," then outputs some effective utility for that world. That is, it's some function A(U(1), U(2), U(3), ... ), where U is the utility of being a certain person. The "worlds" are the different outcomes that you would know the probabilities for if no confusing anthropic things happened (for example, in a coin flip, the probabilities of the heads and tails worlds are 0.5). So the expected utility-ish-stuff is the sum over the worlds of P(world, if there was no anthropic stuff) * A(U(1), U(2), U(3), ... ).

Expected utility, on the other hand, can be written as the sum over people you could be of P(being this person) U(being this person). So the only time when this anthropic decision theory actually maximizes utility is when its expected utility-ish-stuff is proportional to the actual expected utility - that is, you have to pick the correct anthropic preference out of all possible functions, and it can be a different* one for every different problem. The easiest way to do this is simply to know what the expected utility is beforehand, and in that case you might as well just maximize that :P

On the other hand, it's not impossible to find a few useful "A"s, in the occasion that things are really simple. For example, if the different probabilities of being people are all a constant, multiplied by P(world, if there was no anthropic stuff), then the probability of being in a "world" is proportional to the number of people in that world, and the expected utility of being in a world is just the unweighted average of the utility. So the anthropic preference "A" just has to add everything together.

The Sleeping Beauty problem is an example that's simple enough to meet these conditions. There are a few other simple situations that can also yield simple "A" functions. However, if you drift away from simplicity, for example by giving people weak evidence about which world they're in, you can't even always find an "A" that gives you back the expected utility.

Another way to get into trouble is if you use an "A" that you think corresponds to a specific expected utilt, but you have not ever proved this. This was my real beef with Stuart's paper - he names his anthropic preferences with colorful descriptors like "altruistic" or "selfish." This is fine as long as you keep in mind that these names correspond to the anthropic preferences, not to any sort of utility function; but it leads to folly when he goes on to say things like "if we have an altruistic agent," as if he could tell how the agent acted in general. Because the "A" that corresponds to a utility function can change from problem to problem, calling an "A" function "selfish" can't somehow force it to correspond to a utility function we'd call selfish.

So in retrospect calling these anthropic preferences things like "altruistic" was a pretty bad idea, because it generated confusion. This confusion results in him whiffing the Presumptuous Philosopher problem, calling on what "selfish" versus "altruistic" methods of adding utilities together would say, with no guarantee that they have anything to do with any relevant expected utility. In fact, at no point does he ever compare anything to expected utility, because, if you remember back 20 paragraphs ago ( :D ), the whole point of this decision process was to avoid confusing probabilities.