'The only ethical argument for positive 𝛿'?
Andreas Mogensen (Global Priorities Institute, Oxford University)
GPI Working Paper No. 5-2019, published in Philosophical Studies
I consider whether a positive rate of pure intergenerational time preference is justifiable in terms of agent-relative moral reasons relating to partiality between generations, an idea I call discounting for kinship. I respond to Parfit's objections to discounting for kinship, but then highlight a number of apparent limitations of this approach. I show that these limitations largely fall away when we reflect on social discounting in the context of decisions that concern the global community as a whole.
Other working papers
The scope of longtermism – David Thorstad (Global Priorities Institute, University of Oxford)
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…
Meaning, medicine and merit – Andreas Mogensen (Global Priorities Institute, Oxford University)
Given the inevitability of scarcity, should public institutions ration healthcare resources so as to prioritize those who contribute more to society? Intuitively, we may feel that this would be somehow inegalitarian. I argue that the egalitarian objection to prioritizing treatment on the basis of patients’ usefulness to others is best thought…
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…