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
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…
Aggregating Small Risks of Serious Harms – Tomi Francis (Global Priorities Institute, University of Oxford)
According to Partial Aggregation, a serious harm can be outweighed by a large number of somewhat less serious harms, but can outweigh any number of trivial harms. In this paper, I address the question of how we should extend Partial Aggregation to cases of risk, and especially to cases involving small risks of serious harms. I argue that, contrary to the most popular versions of the ex ante and ex post views, we should sometimes prevent a small risk that a large number of people will suffer serious harms rather than prevent…
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 doesn’t 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 evaluation metrics…