Existential Risk and Growth
Philip Trammell (Global Priorities Institute and Department of Economics, University of Oxford) and Leopold Aschenbrenner
GPI Working Paper No. 13-2024
Technologies may pose existential risks to civilization. Though accelerating technological development may increase the risk of anthropogenic existential catastrophe per period in the short run, two considerations suggest that a sector-neutral acceleration decreases the risk that such a catastrophe ever occurs. First, acceleration decreases the time spent at each technology level. Second, since a richer society is willing to sacrifice more for safety, optimal policy can yield an “existential risk Kuznets curve”; acceleration then pulls forward a future in which risk is low. Acceleration typically increases risk only given sufficiently extreme policy failures or direct contributions of acceleration to risk.
An earlier version of the paper was published as GPI Working Paper No. 6-2020, and is available here.
Other working papers
Choosing the future: Markets, ethics and rapprochement in social discounting – Antony Millner (University of California, Santa Barbara) and Geoffrey Heal (Columbia University)
This paper provides a critical review of the literature on choosing social discount rates (SDRs) for public cost-benefit analysis. We discuss two dominant approaches, the first based on market prices, and the second based on intertemporal ethics. While both methods have attractive features, neither is immune to criticism. …
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…
Quadratic Funding with Incomplete Information – Luis M. V. Freitas (Global Priorities Institute, University of Oxford) and Wilfredo L. Maldonado (University of Sao Paulo)
Quadratic funding is a public good provision mechanism that satisfies desirable theoretical properties, such as efficiency under complete information, and has been gaining popularity in practical applications. We evaluate this mechanism in a setting of incomplete information regarding individual preferences, and show that this result only holds under knife-edge conditions. We also estimate the inefficiency of the mechanism in a variety of settings and show, in particular, that inefficiency increases…