Strong longtermism and the challenge from anti-aggregative moral views
Karri Heikkinen (University College London)
GPI Working Paper No. 5 - 2022
Greaves and MacAskill (2019) argue for strong longtermism, according to which, in a wide class of decision situations, the option that is ex ante best, and the one we ex ante ought to choose, is the option that makes the very long-run future go best. One important aspect of their argument is the claim that strong longtermism is compatible with a wide range of ethical assumptions, including plausible non-consequentialist views. In this essay, I challenge this claim. I argue that strong longtermism is incompatible with a range of non-aggregative and partially aggregative moral views. Furthermore, I argue that the conflict between these views and strong longtermism is so deep that those in favour of strong longtermism are better off arguing against them, rather than trying to modify their own view. The upshot of this discussion is that strong longtermism is not as robust to plausible variations in underlying ethical assumptions as Greaves and MacAskill claim. In particular, the stand we take on interpersonal aggregation has important implications on whether making the future go as well as possible should be a global priority.
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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…
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We show that every theory of the value of uncertain prospects must have one of three unpalatable properties. Reckless theories recommend risking arbitrarily great gains at arbitrarily long odds for the sake of enormous potential; timid theories recommend passing up arbitrarily great gains to prevent a tiny increase in risk; nontransitive theories deny the principle that, if A is better than B and B is better than C, then A must be better than C.
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Some worry that advanced artificial agents may resist being shut down. The Incomplete Preferences Proposal (IPP) is an idea for ensuring that does not happen. A key part of the IPP is using a novel ‘Discounted Reward for Same-Length Trajectories (DReST)’ reward function to train agents to (1) pursue goals effectively conditional on each trajectory-length (be ‘USEFUL’), and (2) choose stochastically between different trajectory-lengths (be ‘NEUTRAL’ about trajectory-lengths). In this paper, we propose…