Doomsday rings twice
Andreas Mogensen (Global Priorities Institute, Oxford University)
GPI Working Paper No. 1-2019
This paper considers the argument according to which, because we should regard it as a priori very unlikely that we are among the most important people who will ever exist, we should increase our confidence that the human species will not persist beyond the current historical era, which seems to represent a crucial juncture in human history and perhaps even the history of life on earth. The argument is a descendant of the Carter-Leslie Doomsday Argument, but I show that it does not inherit the crucial flaw in its immediate ancestor. Nonetheless, we are not forced to follow the argument where it leads if we instead significantly decrease our confidence that we can affect the long run future of humanity.
Other working papers
Can an evidentialist be risk-averse? – Hayden Wilkinson (Global Priorities Institute, University of Oxford)
Two key questions of normative decision theory are: 1) whether the probabilities relevant to decision theory are evidential or causal; and 2) whether agents should be risk-neutral, and so maximise the expected value of the outcome, or instead risk-averse (or otherwise sensitive to risk). These questions are typically thought to be independent – that our answer to one bears little on our answer to the other. …
Doomsday and objective chance – Teruji Thomas (Global Priorities Institute, Oxford University)
Lewis’s Principal Principle says that one should usually align one’s credences with the known chances. In this paper I develop a version of the Principal Principle that deals well with some exceptional cases related to the distinction between metaphysical and epistemic modality. I explain how this principle gives a unified account of the Sleeping Beauty problem and chance-based principles of anthropic reasoning…
Estimating long-term treatment effects without long-term outcome data – David Rhys Bernard (Rethink Priorities), Jojo Lee and Victor Yaneng Wang (Global Priorities Institute, University of Oxford)
The surrogate index method allows policymakers to estimate long-run treatment effects before long-run outcomes are observable. We meta-analyse this approach over nine long-run RCTs in development economics, comparing surrogate estimates to estimates from actual long-run RCT outcomes. We introduce the M-lasso algorithm for constructing the surrogate approach’s first-stage predictive model and compare its performance with other surrogate estimation methods. …