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
Exceeding expectations: stochastic dominance as a general decision theory – Christian Tarsney (Global Priorities Institute, Oxford University)
The principle that rational agents should maximize expected utility or choiceworthiness is intuitively plausible in many ordinary cases of decision-making under uncertainty. But it is less plausible in cases of extreme, low-probability risk (like Pascal’s Mugging), and intolerably paradoxical in cases like the St. Petersburg and Pasadena games. In this paper I show that, under certain conditions, stochastic dominance reasoning can capture most of the plausible implications of expectational reasoning while avoiding most of its pitfalls…
Against the singularity hypothesis – David Thorstad (Global Priorities Institute, University of Oxford)
The singularity hypothesis is a radical hypothesis about the future of artificial intelligence on which self-improving artificial agents will quickly become orders of magnitude more intelligent than the average human. Despite the ambitiousness of its claims, the singularity hypothesis has been defended at length by leading philosophers and artificial intelligence researchers. In this paper, I argue that the singularity hypothesis rests on scientifically implausible growth assumptions. …
The Shutdown Problem: An AI Engineering Puzzle for Decision Theorists – Elliott Thornley (Global Priorities Institute, University of Oxford)
I explain and motivate the shutdown problem: the problem of designing artificial agents that (1) shut down when a shutdown button is pressed, (2) don’t try to prevent or cause the pressing of the shutdown button, and (3) otherwise pursue goals competently. I prove three theorems that make the difficulty precise. These theorems suggest that agents satisfying some innocuous-seeming conditions will often try to prevent or cause the pressing of the shutdown button, even in cases where it’s costly to do so. I end by noting that…