The cross-sectional implications of the social discount rate
Maya Eden (Brandeis University)
GPI Working Paper No. 12-2021, published in Econometrica
How should policy discount future returns? The standard approach to this normative question is to ask how much society should care about future generations relative to people alive today. This paper establishes an alternative approach, based on the social desirability of redistributing from the current old to the current young. Along the balanced growth path, bounds on the welfare gains from age-based redistribution imply bounds on the social discount rate. A calibration shows that an objective of maximizing the sum of utilities in each period implies social discount rates that are within a percentage point of the market interest rate.
Other working papers
The asymmetry, uncertainty, and the long term – Teruji Thomas (Global Priorities Institute, Oxford University)
The Asymmetry is the view in population ethics that, while we ought to avoid creating additional bad lives, there is no requirement to create additional good ones. The question is how to embed this view in a complete normative theory, and in particular one that treats uncertainty in a plausible way. After reviewing…
In search of a biological crux for AI consciousness – Bradford Saad (Global Priorities Institute, University of Oxford)
Whether AI systems could be conscious is often thought to turn on whether consciousness is closely linked to biology. The rough thought is that if consciousness is closely linked to biology, then AI consciousness is impossible, and if consciousness is not closely linked to biology, then AI consciousness is possible—or, at any rate, it’s more likely to be possible. A clearer specification of the kind of link between consciousness and biology that is crucial for the possibility of AI consciousness would help organize inquiry into…
AI alignment vs AI ethical treatment: Ten challenges – Adam Bradley (Lingnan University) and Bradford Saad (Global Priorities Institute, University of Oxford)
A morally acceptable course of AI development should avoid two dangers: creating unaligned AI systems that pose a threat to humanity and mistreating AI systems that merit moral consideration in their own right. This paper argues these two dangers interact and that if we create AI systems that merit moral consideration, simultaneously avoiding both of these dangers would be extremely challenging. While our argument is straightforward and supported by a wide range of pretheoretical moral judgments, it has far-reaching…