The Asymmetry, Uncertainty, and the Long Term
Teruji Thomas (Global Priorities Institute, Oxford University)
GPI Working Paper No. 11-2019, published in Philosophy and Phenomenological Research
The Asymmetry is the view in population ethics that, while we ought to avoid creating additional bad lives, there is no requirement to create additional good ones. The question is how to embed this view in a complete normative theory, and in particular one that treats uncertainty in a plausible way. After reviewing the many difficulties that arise in this area, I present general ‘supervenience principles’ that reduce arbitrary choices to uncertainty-free ones. In that sense they provide a method for aggregating across states of nature. But they also reduce arbitrary choices to one-person cases, and in that sense provide a method for aggregating across people. The principles are general in that they are compatible with total utilitarianism and ex post prioritarianism in fixed-population cases, and with a wide range of ways of extending these views to variable-population cases. I then illustrate these principles by writing down a complete theory of the Asymmetry, or rather several such theories to reflect some of the main substantive choice-points. In doing so I suggest a new way to deal with the intransitivity of the relation ‘ought to choose A over B’. Finally, I consider what these views have to say about the importance of extinction risk and the long-run future.
Please note that this working paper contains some additional material about cyclic choice and also about ʽhardʼ versions of the asymmetry, according to which harms to independently existing people cannot be justified by the creation of good lives. But for other material, please refer to and cite the published version in Philosophy and Phenomelogical Research.
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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. …
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