A paradox for tiny probabilities and enormous values
Nick Beckstead (Open Philanthropy Project) and Teruji Thomas (Global Priorities Institute, Oxford University)
GPI Working Paper No. 7-2021, published in Noûs
We show that every theory of the value of uncertain prospects must have one of three unpalatable properties. Reckless theories recommend risking arbitrarily great gains at arbitrarily long odds for the sake of enormous potential; timid theories permit passing up arbitrarily great gains to prevent a tiny increase in risk; non-transitive theories deny the principle that, if A is better than B and B is better than C, then A must be better than C. While non-transitivity has been much discussed, we draw out the costs and benefits of recklessness and timidity when it comes to axiology, decision theory, and moral uncertainty.
Other working papers
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…
Staking our future: deontic long-termism and the non-identity problem – Andreas Mogensen (Global Priorities Institute, Oxford University)
Greaves and MacAskill argue for axiological longtermism, according to which, in a wide class of decision contexts, the option that is ex ante best is the option that corresponds to the best lottery over histories from t onwards, where t is some date far in the future. They suggest that a stakes-sensitivity argument…
Measuring AI-Driven Risk with Stock Prices – Susana Campos-Martins (Global Priorities Institute, University of Oxford)
We propose an empirical approach to identify and measure AI-driven shocks based on the co-movements of relevant financial asset prices. For that purpose, we first calculate the common volatility of the share prices of major US AI-relevant companies. Then we isolate the events that shake this industry only from those that shake all sectors of economic activity at the same time. For the sample analysed, AI shocks are identified when there are announcements about (mergers and) acquisitions in the AI industry, launching of…