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
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…
It Only Takes One: The Psychology of Unilateral Decisions – Joshua Lewis (New York University) et al.
Sometimes, one decision can guarantee that a risky event will happen. For instance, it only took one team of researchers to synthesize and publish the horsepox genome, thus imposing its publication even though other researchers might have refrained for biosecurity reasons. We examine cases where everybody who can impose a given event has the same goal but different information about whether the event furthers that goal. …
Towards shutdownable agents via stochastic choice – Elliott Thornley (Global Priorities Institute, University of Oxford), Alexander Roman (New College of Florida), Christos Ziakas (Independent), Leyton Ho (Brown University), and Louis Thomson (University of Oxford)
Some worry that advanced artificial agents may resist being shut down. The Incomplete Preferences Proposal (IPP) is an idea for ensuring that does not happen. A key part of the IPP is using a novel ‘Discounted Reward for Same-Length Trajectories (DReST)’ reward function to train agents to (1) pursue goals effectively conditional on each trajectory-length (be ‘USEFUL’), and (2) choose stochastically between different trajectory-lengths (be ‘NEUTRAL’ about trajectory-lengths). In this paper, we propose…