How cost-effective are efforts to detect near-Earth-objects?
Toby Newberry (Future of Humanity Institute, University of Oxford)
GPI Technical Report No. T1-2021
Near-Earth-objects (NEOs) include asteroids and comets with orbits that bring them into close proximity with Earth. NEOs are well-known to have impacted Earth in the past, sometimes to catastrophic effect.2 Over the past few decades, humanity has taken steps to detect any NEOs on impact trajectories, and, in doing so, we have significantly improved our estimate of the risk that an impact will occur over the next century. This report estimates the cost-effectiveness of such detection efforts. The remainder of this section sets out the context of the report...
Other working papers
A Fission Problem for Person-Affecting Views – Elliott Thornley (Global Priorities Institute, University of Oxford)
On person-affecting views in population ethics, the moral import of a person’s welfare depends on that person’s temporal or modal status. These views typically imply that – all else equal – we’re never required to create extra people, or to act in ways that increase the probability of extra people coming into existence. In this paper, I use Parfit-style fission cases to construct a dilemma for person-affecting views: either they forfeit their seeming-advantages and face fission analogues…
Maximal cluelessness – Andreas Mogensen (Global Priorities Institute, Oxford University)
I argue that many of the priority rankings that have been proposed by effective altruists seem to be in tension with apparently reasonable assumptions about the rational pursuit of our aims in the face of uncertainty. The particular issue on which I focus arises from recognition of the overwhelming importance…
Imperfect Recall and AI Delegation – Eric Olav Chen (Global Priorities Institute, University of Oxford), Alexis Ghersengorin (Global Priorities Institute, University of Oxford) and Sami Petersen (Department of Economics, University of Oxford)
A principal wants to deploy an artificial intelligence (AI) system to perform some task. But the AI may be misaligned and aim to pursue a conflicting objective. The principal cannot restrict its options or deliver punishments. Instead, the principal is endowed with the ability to impose imperfect recall on the agent. The principal can then simulate the task and obscure whether it is real or part of a test. This allows the principal to screen misaligned AIs during testing and discipline their behaviour in deployment. By increasing the…