The scope of longtermism
David Thorstad (Global Priorities Institute, University of Oxford)
GPI Working Paper No. 6-2021
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 cause-neutral philanthropic decisionmaking, it is increasingly suggested that longtermism holds in many or most decision problems that humans face. By contrast, I suggest that the scope of longtermism may be more restricted than commonly supposed. After specifying my target, swamping axiological strong longtermism (swamping ASL), I give two arguments for the rarity thesis that the options needed to vindicate swamping ASL in a given decision problem are rare. I use the rarity thesis to pose two challenges to the scope of longtermism: the area challenge that swamping ASL often fails when we restrict our attention to specific cause areas, and the challenge from option unawareness that swamping ASL may fail when decision problems are modified to incorporate agents’ limited awareness of the options available to them.
Other working papers
Social Beneficence – Jacob Barrett (Global Priorities Institute, University of Oxford)
A background assumption in much contemporary political philosophy is that justice is the first virtue of social institutions, taking priority over other values such as beneficence. This assumption is typically treated as a methodological starting point, rather than as following from any particular moral or political theory. In this paper, I challenge this assumption.
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…
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. …