The paralysis argument
William MacAskill, Andreas Mogensen (Global Priorities Institute, Oxford University)
GPI Working Paper No. 6-2019, published in Philosophers’ Imprint
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 a number of objections. We then suggest what we believe is the most promising response: to accept, in practice, a highly demanding morality of beneficence with a long-term focus.
Other working papers
Longtermist institutional reform – Tyler M. John (Rutgers University) and William MacAskill (Global Priorities Institute, Oxford University)
There is a vast number of people who will live in the centuries and millennia to come. Even if homo sapiens survives merely as long as a typical species, we have hundreds of thousands of years ahead of us. And our future potential could be much greater than that again: it will be hundreds of millions of years until the Earth is sterilized by the expansion of the Sun, and many trillions of years before the last stars die out. …
When should an effective altruist donate? – William MacAskill (Global Priorities Institute, Oxford University)
Effective altruism is the use of evidence and careful reasoning to work out how to maximize positive impact on others with a given unit of resources, and the taking of action on that basis. It’s a philosophy and a social movement that is gaining considerable steam in the philanthropic world. For example,…
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…