Time Bias and Altruism
Leora Urim Sung (University College London)
GPI Working Paper No. 17-2023, winner of the ECCP 2022 Paper Prize
We are typically near-future biased, being more concerned with our near future than our distant future. This near-future bias can be directed at others too, being more concerned with their near future than their distant future. In this paper, I argue that, because we discount the future in this way, beyond a certain point in time, we morally ought to be more concerned with the present well- being of others than with the well-being of our distant future selves. It follows that we morally ought to sacrifice our distant-future well-being in order to relieve the present suffering of others. I argue that this observation is particularly relevant for the ethics of charitable giving, as the decision to give to charity usually means a reduction in our distant-future well-being rather than our immediate well-being.
Other working papers
A bargaining-theoretic approach to moral uncertainty – Owen Cotton-Barratt (Future of Humanity Institute, Oxford University), Hilary Greaves (Global Priorities Institute, Oxford University)
This paper explores a new approach to the problem of decision under relevant moral uncertainty. We treat the case of an agent making decisions in the face of moral uncertainty on the model of bargaining theory, as if the decision-making process were one of bargaining among different internal parts of the agent, with different parts committed to different moral theories. The resulting approach contrasts interestingly with the extant “maximise expected choiceworthiness”…
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. …
Imperfect Recall and AI Delegation – Eric Olav Chen (Global Priorities Institute, University of Oxford), Alexis Ghersengorin (Global Priorities Institute, University of Oxford) and Sami Petersen (Department of Economics, University of Oxford)
A principal wants to deploy an artificial intelligence (AI) system to perform some task. But the AI may be misaligned and aim to pursue a conflicting objective. The principal cannot restrict its options or deliver punishments. Instead, the principal is endowed with the ability to impose imperfect recall on the agent. The principal can then simulate the task and obscure whether it is real or part of a test. This allows the principal to screen misaligned AIs during testing and discipline their behaviour in deployment. By increasing the…