Andreas Mogensen, a Senior Research Fellow at the Global Priorities Institute, has just published a draft of a paper on "Maximal Cluelessness". Abstract:
I argue that many of the priority rankings that have been proposed by effective altruists seem to be in tension with apparently reasonable assumptions about the rational pursuit of our aims in the face of uncertainty. The particular issue on which I focus arises from recognition of the overwhelming importance and inscrutability of the indirect effects of our actions, conjoined with the plausibility of a permissive decision principle governing cases of deep uncertainty, known as the maximality rule. I conclude that we lack a compelling decision theory that is consistent with a long-termist perspective and does not downplay the depth of our uncertainty while supporting orthodox effective altruist conclusions about cause prioritization.
Also, when reading Greaves and Mogensen's papers, I was reminded of the ideas of cluster thinking (also here) and model combination. I could be drawing faulty analogies, but it seemed like those ideas could be ways to capture, in a form that can actually be readily worked with, the following idea (from Greaves; the same basic concept is also used in Mogensen):
That is, we can consider each probability function in the agent's representor as one model, and then either qualitatively use Holden's idea of cluster thinking, or get a weighted combination of those models. Then we'd actually have an answer, rather than just indifference.
This seems like potentially "the best of both worlds"; i.e., a way to capture both of the following intuitively appealing ideas: