How many lives does the future hold?
Toby Newberry (Future of Humanity Institute, University of Oxford)
GPI Technical Report No. T2-2021
The total number of people who have ever lived, across the entire human past, has been estimated at around 100 billion.2 The total number of people who will ever live, across the entire human future, is unknown - but not immune to the tools of rational inquiry. This report estimates the expected size of the future, as measured in units of ‘human-life-equivalents’ (henceforth: ‘lives’). The task is a daunting one, and the aim here is not to be the final word on this subject. Instead, this report aspires to two more modest aims...
Other working papers
Tough enough? Robust satisficing as a decision norm for long-term policy analysis – Andreas Mogensen and David Thorstad (Global Priorities Institute, Oxford University)
This paper aims to open a dialogue between philosophers working in decision theory and operations researchers and engineers whose research addresses the topic of decision making under deep uncertainty. Specifically, we assess the recommendation to follow a norm of robust satisficing when making decisions under deep uncertainty in the context of decision analyses that rely on the tools of Robust Decision Making developed by Robert Lempert and colleagues at RAND …
Time Bias and Altruism – Leora Urim Sung (University College London)
We are typically near-future biased, being more concerned with our near future than our distant future. This near-future bias can be directed at others too, being more concerned with their near future than their distant future. In this paper, I argue that, because we discount the future in this way, beyond a certain point in time, we morally ought to be more concerned with the present well- being of others than with the well-being of our distant future selves. It follows that we morally ought to sacrifice…
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. …