The Significance, Persistence, Contingency Framework
William MacAskill, Teruji Thomas (Global Priorities Institute, University of Oxford) and Aron Vallinder (Forethought Foundation for Global Priorities Institute)
GPI Technical Report No. T1-2022
The world, considered from beginning to end, combines many different features, or states of affairs, that contribute to its value. The value of each feature can be factored into its significance—its average value per unit time—and its persistence—how long it lasts. Sometimes, though, we want to ask a further question: how much of the feature’s value can be attributed to a particular agent’s decision at a particular point in time (or to some other originating event)? In other words, to what extent is the feature’s value contingent on the agent’s choice? For this, we must also look at the counterfactual: how would things have turned out otherwise?
Other working papers
Heuristics for clueless agents: how to get away with ignoring what matters most in ordinary decision-making – David Thorstad and Andreas Mogensen (Global Priorities Institute, Oxford University)
Even our most mundane decisions have the potential to significantly impact the long-term future, but we are often clueless about what this impact may be. In this paper, we aim to characterize and solve two problems raised by recent discussions of cluelessness, which we term the Problems of Decision Paralysis and the Problem of Decision-Making Demandingness. After reviewing and rejecting existing solutions to both problems, we argue that the way forward is to be found in the distinction between procedural and substantive rationality…
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…