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
Three mistakes in the moral mathematics of existential risk – David Thorstad (Global Priorities Institute, University of Oxford)
Longtermists have recently argued that it is overwhelmingly important to do what we can to mitigate existential risks to humanity. I consider three mistakes that are often made in calculating the value of existential risk mitigation: focusing on cumulative risk rather than period risk; ignoring background risk; and neglecting population dynamics. I show how correcting these mistakes pushes the value of existential risk mitigation substantially below leading estimates, potentially low enough to…
The paralysis argument – William MacAskill, Andreas Mogensen (Global Priorities Institute, Oxford University)
Given plausible assumptions about the long-run impact of our everyday actions, we show that standard non-consequentialist constraints on doing harm entail that we should try to do as little as possible in our lives. We call this the Paralysis Argument. After laying out the argument, we consider and respond to…
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…