Respect for others' risk attitudes and the long-run future

Andreas Mogensen (Global Priorities Institute, University of Oxford)

GPI Working Paper No. 20-2022, published in Noûs

When our choice affects some other person and the outcome is unknown, it has been argued that we should defer to their risk attitude, if known, or else default to use of a risk avoidant risk function. This, in turn, has been claimed to require the use of a risk avoidant risk function when making decisions that primarily affect future people, and to decrease the desirability of efforts to prevent human extinction, owing to the significant risks associated with continued human survival. I raise objections to the claim that respect for others’ risk attitudes requires risk avoidance when choosing for future generations. In particular, I argue that there is no known principle of interpersonal aggregation that yields acceptable results in variable population contexts and is consistent with a plausible ideal of respect for others’ risk attitudes in fixed population cases.

Other working papers

Meaning, medicine and merit – Andreas Mogensen (Global Priorities Institute, Oxford University)

Given the inevitability of scarcity, should public institutions ration healthcare resources so as to prioritize those who contribute more to society? Intuitively, we may feel that this would be somehow inegalitarian. I argue that the egalitarian objection to prioritizing treatment on the basis of patients’ usefulness to others is best thought…

The cross-sectional implications of the social discount rate – Maya Eden (Brandeis University)

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. …

Will AI Avoid Exploitation? – Adam Bales (Global Priorities Institute, University of Oxford)

A simple argument suggests that we can fruitfully model advanced AI systems using expected utility theory. According to this argument, an agent will need to act as if maximising expected utility if they’re to avoid exploitation. Insofar as we should expect advanced AI to avoid exploitation, it follows that we should expected advanced AI to act as if maximising expected utility. I spell out this argument more carefully and demonstrate that it fails, but show that the manner of its failure is instructive…