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
The case for strong longtermism – Hilary Greaves and William MacAskill (Global Priorities Institute, University of Oxford)
A striking fact about the history of civilisation is just how early we are in it. There are 5000 years of recorded history behind us, but how many years are still to come? If we merely last as long as the typical mammalian species…
How to neglect the long term – Hayden Wilkinson (Global Priorities Institute, University of Oxford)
Consider longtermism: the view that, at least in some of the most important decisions facing agents today, which options are morally best is determined by which are best for the long-term future. Various critics have argued that longtermism is false—indeed, that it is obviously false, and that we can reject it on normative grounds without close consideration of certain descriptive facts. In effect, it is argued, longtermism would be false even if real-world agents had promising means…
Estimating long-term treatment effects without long-term outcome data – David Rhys Bernard (Rethink Priorities), Jojo Lee and Victor Yaneng Wang (Global Priorities Institute, University of Oxford)
The surrogate index method allows policymakers to estimate long-run treatment effects before long-run outcomes are observable. We meta-analyse this approach over nine long-run RCTs in development economics, comparing surrogate estimates to estimates from actual long-run RCT outcomes. We introduce the M-lasso algorithm for constructing the surrogate approach’s first-stage predictive model and compare its performance with other surrogate estimation methods. …