The scope of longtermism
David Thorstad (Global Priorities Institute, University of Oxford)
GPI Working Paper No. 6-2021
Longtermism holds roughly that in many decision situations, the best thing we can do is what is best for the long-term future. The scope question for longtermism asks: how large is the class of decision situations for which longtermism holds? Although longtermism was initially developed to describe the situation of cause-neutral philanthropic decisionmaking, it is increasingly suggested that longtermism holds in many or most decision problems that humans face. By contrast, I suggest that the scope of longtermism may be more restricted than commonly supposed. After specifying my target, swamping axiological strong longtermism (swamping ASL), I give two arguments for the rarity thesis that the options needed to vindicate swamping ASL in a given decision problem are rare. I use the rarity thesis to pose two challenges to the scope of longtermism: the area challenge that swamping ASL often fails when we restrict our attention to specific cause areas, and the challenge from option unawareness that swamping ASL may fail when decision problems are modified to incorporate agents’ limited awareness of the options available to them.
Other working papers
The cross-sectional implications of the social discount rate – Maya Eden (Brandeis University)
How should policy discount future returns? The standard approach to this normative question is to ask how much society should care about future generations relative to people alive today. This paper establishes an alternative approach, based on the social desirability of redistributing from the current old to the current young. …
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. …
Quadratic Funding with Incomplete Information – Luis M. V. Freitas (Global Priorities Institute, University of Oxford) and Wilfredo L. Maldonado (University of Sao Paulo)
Quadratic funding is a public good provision mechanism that satisfies desirable theoretical properties, such as efficiency under complete information, and has been gaining popularity in practical applications. We evaluate this mechanism in a setting of incomplete information regarding individual preferences, and show that this result only holds under knife-edge conditions. We also estimate the inefficiency of the mechanism in a variety of settings and show, in particular, that inefficiency increases…