Longtermist institutional reform
Tyler M. John (Rutgers University) and William MacAskill (Global Priorities Institute, Oxford University)
GPI Working Paper No. 14-2020, published in The Long View: Essays on Policy, Philanthropy, and the Long-Term Future
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. In all probability, future generations will outnumber us by thousands or millions to one; of all the people who we might affect with our actions, the overwhelming majority are yet to come. [...]
Other working papers
Simulation expectation – Teruji Thomas (Global Priorities Institute, University of Oxford)
I present a new argument for the claim that I’m much more likely to be a person living in a computer simulation than a person living in the ground-level of reality. I consider whether this argument can be blocked by an externalist view of what my evidence supports, and I urge caution against the easy assumption that actually finding lots of simulations would increase the odds that I myself am in one.
Can an evidentialist be risk-averse? – Hayden Wilkinson (Global Priorities Institute, University of Oxford)
Two key questions of normative decision theory are: 1) whether the probabilities relevant to decision theory are evidential or causal; and 2) whether agents should be risk-neutral, and so maximise the expected value of the outcome, or instead risk-averse (or otherwise sensitive to risk). These questions are typically thought to be independent – that our answer to one bears little on our answer to the other. …
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…